2408.01933v1

# DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models **Authors**: {, , }, , {, , } Abstract Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings. We thus introduce the diagnostic reasoning dataset for clinical notes (DiReCT), aiming at evaluating the reasoning ability and interpretability of LLMs compared to human doctors. It contains 511 clinical notes, each meticulously annotated by physicians, detailing the diagnostic reasoning process from observations in a clinical note to the final diagnosis. Additionally, a diagnostic knowledge graph is provided to offer essential knowledge for reasoning, which may not be covered in the training data of existing LLMs. Evaluations of leading LLMs on DiReCT bring out a significant gap between their reasoning ability and that of human doctors, highlighting the critical need for models that can reason effectively in real-world clinical scenarios Code are available https://github.com/wbw520/DiReCT. Data will be released through PhysioNet.. footnotetext: Corresponding author. 1 Introduction Recent advancements of large language models (LLMs) [Zhao et al., 2023] have ushered in new possibilities and challenges for a wide range of natural language processing (NLP) tasks [Min et al., 2023]. In the medical domain, these models have demonstrated remarkable prowess [Anil et al., 2023, Han et al., 2023], particularly in medical question answering (QA) [Jin et al., 2021]. Leading-edge models, such as GPT-4 [OpenAI, 2023a], exhibit profound proficiency in understanding and generating text [Bubeck et al., 2023], even achieved high scores on the United States Medical Licensing Examination (USMLE) questions [Nori et al., 2023]. Despite the advancements, interpretability is critical, particularly in medical NLP tasks [Liévin et al., 2024]. Some studies assess this capability over medical QA [Pal et al., 2022, Li et al., 2023, Chen et al., 2024] or natural language inference (NLI) [Jullien et al., 2023]. Putting more attention on interpretability, they use relatively simple tasks as testbeds, taking short text as input. However, tasks in real clinical settings can be more complex [Gao et al., 2023a]. As shown in Figure 1, a typical diagnosis requires comprehending and combining various information, such as health records, physical examinations, and laboratory tests, for further reasoning of possible diseases in a step-by-step manner following the established guidelines. This observation suggests that both perception, or reading, (e.g., finding necessary information in medical record) and reasoning (determining the disease based on the observations) should be counted when evaluating interpretability in LLM-based medical NLP tasks. For a more comprehensive evaluation of LLMs for supporting diagnosis in a more realistic setting, we propose a Di agnostic Re asoning dataset for C linical no T es (DiReCT). The task basically is predicting the diagnosis from a clinical note of a patient, which is a collection of various medical records, written in natural language. Our dataset contains 511 clinical notes spanning 25 disease categories, sampled from a publicly available database, MIMIC-IV [Johnson et al., 2023]. Each clinical note undergoes fine-grained annotation by professional physicians. The annotators (i.e., the physicians) are responsible for identifying the text, or the observation, in the note that leads to a certain diagnosis, as well as the explanation. The dataset also provides a diagnostic knowledge graph based on existing diagnostic guidelines to facilitate more consistent annotations and to supply a model with essential knowledge for reasoning that might not be encompassed in its training data. To underscore the challenge offered by our dataset, we evaluate a simple AI-agent based baseline [Xi et al., 2023, Tang et al., 2023] that utilizes the knowledge graph to decompose the diagnosis into a sequence of diagnoses from a smaller number of observations. Our experimental findings indicate that current state-of-the-art LLMs still fall short of aligning well with human doctors. Contribution. DiReCT offers a new challenge in diagnosis from a complex clinical note with explicit knowledge of established guidelines. This challenge aligns with a realistic medical scenario that doctors are experiencing. In the application aspect, the dataset facilitates the development of a model to support doctors in diagnosis, which is error-prone [Middleton et al., 2013, Liu et al., 2022]. From the technical aspect, the dataset can benchmark models’ ability to read long text and find necessary observations for multi-evidence entailment tree reasoning. As shown in Figure 3, this is not trivial because of the variations in writing; superficial matching does not help, and medical knowledge is vital. Meanwhile, reasoning itself is facilitated by the knowledge graph. The model does not necessarily have the knowledge of diagnostic guidelines. With this choice, the knowledge graph explains the reasoning process, which is also beneficial when deploying such a diagnosis assistant system in practical uses. <details> <summary>x1.png Details</summary>  ### Visual Description ## Diagram: Stroke Diagnosis Procedure ### Overview The image is a diagram illustrating the typical steps in diagnosing a stroke, starting from patient admission and ending with a final diagnosis of hemorrhagic stroke. The diagram uses icons and text to represent each stage of the process. ### Components/Axes * **Stages:** The diagram depicts the following stages in sequence: * Admission * Consultation * Examination * Final Diagnosis * **Textual Information:** Each stage is accompanied by relevant textual information, such as chief complaints, medical history, and radiology findings. * **Icons:** Each stage is represented by an icon. * **Flow:** The flow of the diagnosis procedure is indicated by arrows. * **Labels:** "Diagnosis Procedure" is written along a dashed arrow at the bottom of the diagram. ### Detailed Analysis or ### Content Details 1. **Admission:** * Icon: A person with a heart symbol and an ambulance. * Text: "Admission" 2. **Consultation:** * Icon: A hospital building and a magnifying glass over a person icon. * Text: "Consultation" 3. **Examination:** * Icon: A doctor icon with a speech bubble labeled "Suspected Stroke". * Text: * "Chief Complaint: Right weakness and aphasia." * "Events: He had episode of maurosis fugax in right eye ******** ago ....." * "Past Medical History: HTN, COPD on home 1L ....." 4. **Examination:** * Icon: A CT scan machine. * Text: * "Radiology: A 3.0 x 1.1 cm left thalamic hematoma appears stable when ....." * "MR HEAD: Only ***** T1, axial T1, and axial FLAIR sequences were ....." * "CT HEAD: Stable **** basal ganglia ....." 5. **Final Diagnosis:** * Icon: A doctor icon with a speech bubble labeled "Hemorrhagic Stroke". * Text: "Final Diagnosis" ### Key Observations * The diagram outlines a sequential process from initial patient admission to a final diagnosis. * The textual information provides details about the patient's symptoms, medical history, and examination findings. * The icons visually represent each stage of the diagnosis procedure. ### Interpretation The diagram illustrates the steps involved in diagnosing a stroke, highlighting the importance of patient history, physical examination, and imaging studies. The progression from "Suspected Stroke" to "Hemorrhagic Stroke" suggests a diagnostic pathway based on the presented information. The diagram emphasizes the role of various medical professionals and diagnostic tools in reaching a final diagnosis. </details> Figure 1: When a patient is admitted, an initial consultation takes place to collect subjective information. Subsequent observations may then require further examination to confirm the diagnosis. 2 Related Works Natural language explanation. Recent advancements in NLP have led to significant achievements [Min et al., 2023]. However, existing models often lack explainability, posing potential risks [Danilevsky et al., 2020, Gurrapu et al., 2023]. Numerous efforts have been made to address this challenge. One effective approach is to provide a human-understandable plain text explanation alongside the model’s output [Camburu et al., 2018, Rajani et al., 2019]. Another strategy involves identifying evidence within the input that serves as a rationale for the model’s decisions, aligning with human reasoning [DeYoung et al., 2020]. Expanding on this concept, [Jhamtani and Clark, 2020] introduces chain-structured explanations, given that a diagnosis can demand multi-hop reasoning. This idea is further refined by ProofWriter [Tafjord et al., 2021] through a proof stage for explanations, and by [Zhao et al., 2021] through retrieval from a corpus. [Dalvi et al., 2021] proposes the entailment tree, offering more detailed explanations and facilitating inspection of the model’s reasoning. More recently, [Zhang et al., 2024] employed cumulative reasoning to tap into the potential of LLMs to provide explanation via a directed acyclic graph. Although substantial progress has been made, interpreting NLP tasks in medical domains remains an ongoing challenge [Liévin et al., 2024]. Benchmarks of interpretability in the medical domain Several datasets are designed to assess a model’s reasoning together with its interpretability in medical NLP (Table 1). MedMCQA [Pal et al., 2022] and other medical QA datasets [Li et al., 2023, Chen et al., 2024] provide plain text as explanations for QA tasks. NLI4CT [Jullien et al., 2023] uses clinical trial reports, focusing on NLI supported by multi-hop reasoning. N2N2 [Gao et al., 2022] proposes a summarization (Sum) task for a diagnosis based on multiple pieces of evidence in the input clinical note. NEJM CPC [Zack et al., 2023] interprets clinicians’ diagnostic reasoning as plain text for reasoning clinical diagnosis (CD). DR.BENCH [Gao et al., 2023b] aggregates publicly available datasets to assess the diagnostic reasoning of LLMs. Utilizing an multi-evidence entailment tree explanation, DiReCT introduces a more rigorous task to assess whether LLMs can align with doctors’ reasoning in real clinical settings. Table 1: Comparison of existing datasets for medical reasoning tasks and ours. “t” and “w” mean tokens and words for the length of input, respectively. | Dataset | Task | Data Source | Length | Explanation | # Cases | | --- | --- | --- | --- | --- | --- | | MedMCQA [Pal et al., 2022] | QA | Examination | 9.93 t | Plain Text | 194,000 | | ExplainCPE [Li et al., 2023] | QA | Examination | 37.79 w | Plain Text | 7,000 | | JAMA Challenge [Chen et al., 2024] | QA | Clinical Cases | 371 w | Plain Text | 1,524 | | Medbullets [Chen et al., 2024] | QA | Online Questions | 163 w | Plain Text | 308 | | N2N2 [Gao et al., 2022] | Sum | Clinical Notes | 785.46 t | Evidences | 768 | | NLI4CT [Jullien et al., 2023] | NLI | Clinical Trail Reports | 10-35 t | Multi-hop | 2,400 | | NEJM CPC [Zack et al., 2023] | CD | Clinical Cases | - | Plain Text | 2,525 | | DiReCT (Ours) | CD | Clinical Notes | 1074.6 t | Entailment Tree | 511 | 3 A benchmark for Clinical Notes Diagnosis This section first detail clinical notes (Section 3.1). We also describes the knowledge graph that encodes existing guidelines (Section 3.2). Our task definition, which tasks a clinical note and the knowledge graph as input is given in Section 3.4. We then present our annotation process for clinical notes (Section 3.3) and the evaluation metrics (Section 3.5). 3.1 Clinical Notes Clinical notes used in DiReCT are stored in the SOAP format [Weed, 1970]. A clinical note comprises four components: In the subjective section, the physician records the patient’s chief complaint, the history of present illness, and other subjective experiences reported by the patient. The objective section contains structural data obtained through examinations (inspection, auscultation, etc.) and other measurable means. The assessment section involves the physician’s analysis and evaluation of the patient’s condition. This may include a summary of current status, etc. Finally, the plan section outlines the physician’s proposed treatment and management plan. This may include prescribed medications, recommended therapies, and further investigations. A clinical note also includes a primary discharge diagnosis (PDD) in the assessment section. DiReCT’s clinical notes are sourced from the MIMIC-IV dataset [Johnson et al., 2023] (PhysioNet Credentialed Health Data License 1.5.0), which encompasses over 40,000 patients admitted to the intensive care units. Each note contains clinical data for a patient. To construct DiReCT, we curated a subset of 511 notes whose PDDs fell within one of 25 disease categories $i$ in 5 medical domains. In our task, a note $R=\{r\}$ is an excerpt of 6 clinical data in the subjective and objective sections (i.e., $|R|=6$ ): chief complaint, history of present illness, past medical history, family history, physical exam, and pertinent results. We excluded data, such as review system and social history, because they are often missing in the original clinical notes and are less relevant to the diagnosis. We also identified the PDD $d^{\star}$ associated with $R$ . All clinical notes in DiReCT are related to only one PDD, and there is no secondary discharge diagnosis. The set of $d^{\star}$ ’s for all $R$ ’s collectively forms $\mathcal{D}^{\star}$ . We manually removed any descriptions that disclose the PDD in $R$ . 3.2 Diagnostic Knowledge Graph Existing knowledge graphs for the medical domain, e.g., UMLS KG [Bodenreider, 2004], lack the ability to provide specific clinical decision support (e.g., diagnostic threshold, context-specific data, dosage information, etc.), which are critical for accurate diagnosis. Our knowledge graphs $\mathcal{K}=\{\mathcal{K}_{i}\}_{i}$ is a collection of graph $\mathcal{K}_{i}$ for disease category $i$ . $\mathcal{K}_{i}$ is based on the diagnosis criteria in existing guidelines (refer to supplementary material for details). $\mathcal{K}_{i}$ ’s nodes are either premise $p∈\mathcal{P}_{i}$ (medical statement, e.g., Headache is a symptom of) and diagnoses $d∈\mathcal{D}_{i}$ (e.g., Suspected Stroke). $\mathcal{K}_{i}$ consists of two different types of edges. One is premise-to-diagnosis edges $\mathcal{S}_{i}=\{(p,d)\}$ , where $p∈\mathcal{P}_{i}$ and $d∈\mathcal{D}_{i}$ ; an edge is from $p$ to $d$ . This edge represents the necessary premise $p$ to make a diagnosis $d$ . We refer to them as supporting edges. The other is diagnosis-to-diagnosis edges $\mathcal{F}_{i}=\{(d,d^{\prime})\}$ , where $d,d^{\prime}∈\mathcal{D}_{i}$ and the edge is from $d$ to $d^{\prime}$ , which represents the diagnostic flow. These edges are referred to as procedural edges. A disease category is defined according to an existing guideline, which starts from a certain diagnosis; therefore, a procedural graph $\mathcal{G}_{i}=(\mathcal{D}_{i},\mathcal{F}_{i})$ has only one root node and arbitrarily branches toward multiple leaf nodes that represent PDDs (i.e., the clinical notes in DiReCT are chosen to cover all leaf nodes of $\mathcal{G}_{i}$ ). Thus, $\mathcal{G}_{i}$ is a tree. We denote the set of the leaf nodes (or PDDs) as $\mathcal{D}^{\star}_{i}⊂\mathcal{D}_{i}$ . The knowledge graph is denoted by $\mathcal{K}_{i}=(\mathcal{D}_{i},\mathcal{P}_{i},\mathcal{S}_{i},\mathcal{F}_{% i})$ . <details> <summary>x2.png Details</summary>  ### Visual Description ## Flow Diagram: Acute Coronary Syndrome (ACS) Diagnosis ### Overview The image is a flow diagram illustrating the diagnostic process for Acute Coronary Syndrome (ACS). It shows the progression from initial symptoms and findings to specific diagnoses like STEMI-ACS, NSTEMI-ACS, NSTE-ACS, and UA. The diagram uses boxes to represent conditions or diagnostic categories, and arrows to indicate the flow of the diagnostic process. ### Components/Axes * **Boxes:** Represent different stages or diagnoses in the ACS diagnostic pathway. Boxes are either light blue or light gray. * **Arrows:** Indicate the flow of the diagnostic process. Black arrows indicate a direct progression, while pink arrows indicate a possible progression. * **Text Labels:** Describe the conditions, symptoms, or diagnostic criteria associated with each box. ### Detailed Analysis or ### Content Details 1. **Initial Symptoms/Findings (Top-Left):** * "Breathlessness is a symptom..." (light blue box, top-left) * "Arrhythmias is..." (light blue box, left) * "Third Heart Sound..." (light blue box, bottom-left) * "Any Severe Presentations..." (light blue box, bottom) 2. **Initial Assessment:** * "Suspected ACS" (light gray box, center-left). This box receives arrows from "Breathlessness is a symptom...", "Arrhythmias is...", and "Third Heart Sound...". 3. **Further Assessment:** * "Strongly Suspected ACS" (light gray box, center). This box receives an arrow from "Suspected ACS" (pink arrow) and "Any Severe Presentations...". 4. **Diagnostic Criteria (Top):** * "ST Elevation is criteria..." (light blue box, top) * "hs-cTn Exceeded..." (light blue box, top-right) * "Cardiac Troponin ↑" (light blue box, top-right) * "non-ST Elevation..." (light blue box, bottom) * "No Obvious ECG..." (light blue box, bottom-right) 5. **Specific Diagnoses:** * "STEMI-ACS" (light gray box, top-center). This box receives arrows from "ST Elevation is criteria..." and "Strongly Suspected ACS" (pink arrow). * "NSTE-ACS" (light gray box, center). This box receives arrows from "non-ST Elevation..." and "Strongly Suspected ACS" (pink arrow). * "NSTEMI-ACS" (light gray box, right). This box receives arrows from "hs-cTn Exceeded..." and "Cardiac Troponin ↑". * "UA" (light gray box, bottom-right). This box receives arrows from "NSTE-ACS" (pink arrow) and "No Obvious ECG...". ### Key Observations * The diagram illustrates a step-by-step approach to diagnosing ACS, starting with initial symptoms and progressing to specific diagnoses based on diagnostic criteria. * The pink arrows indicate possible progressions or relationships between stages. * The light blue boxes represent symptoms, findings, or criteria, while the light gray boxes represent diagnostic categories. ### Interpretation The flow diagram provides a visual representation of the diagnostic pathway for Acute Coronary Syndrome (ACS). It highlights the importance of considering various symptoms, findings, and diagnostic criteria in order to arrive at an accurate diagnosis. The diagram suggests that the diagnostic process involves a series of assessments and evaluations, with the ultimate goal of classifying the patient's condition into one of several categories: STEMI-ACS, NSTEMI-ACS, NSTE-ACS, or UA. The pink arrows indicate that the diagnostic process is not always linear and that there may be multiple possible pathways to a final diagnosis. </details> Figure 2: A part of $\mathcal{K}_{i}$ for $i$ being Acute Coronary Syndromes. Figure 2 shows a part of $\mathcal{K}_{i}$ , where $i$ is Acute Coronary Syndromes (ACS). Premises in $\mathcal{P}_{i}$ and diagnoses in $\mathcal{D}_{i}$ are given in the blue and gray boxes, while PDDs in $\mathcal{D}^{\star}_{i}$ are ones without outgoing edges (i.e., STEMI-ACS and NSTEMI-ACS, and UA). The black and red arrows are edges in $\mathcal{S}$ and $\mathcal{F}$ , respectively, where the black arrows indicate the supporting edges. $\mathcal{K}$ serves two essential functions: (1) They serve as the gold standard for annotation, guiding doctors in the precise and uniform interpretation of clinical notes. (2) Our task also allows a model to use them to ensure the output from an LLM can be closely aligned with the reasoning processes of medical professionals. 3.3 Data Annotation Let $d^{\star}∈\mathcal{D}^{\star}_{i}$ denote the PDD of disease category $i$ associated with $R$ . We can find a subgraph $\mathcal{K}_{i}(d^{\star})$ of $\mathcal{K}_{i}$ that contains all ancestors of $d^{\star}$ , including premises in $\mathcal{P}_{i}$ . We also denote the set of supporting edges in $\mathcal{K}_{i}(d^{\star})$ as $\mathcal{S}_{i}(d^{\star})$ . Our annotation process is, for each supporting edge $(p,d)∈\mathcal{S}_{i}(d^{\star})$ , to extract observation $o∈\mathcal{O}$ in $R$ (highlighted text in the clinical note in Figure 3) and provide rationalization $z$ of this deduction why $o$ is a support for $d$ or corresponds to $p$ . All annotations strictly follow the procedural flow in $\mathcal{K}_{i}$ , and each observation is only related to one diagnostic node. If $R$ does not provide sufficient observations for the PDD (which may happen when a certain test is omitted), the annotators were asked to add plausible observations to $R$ . This choice compromises the fidelity of our dataset to the original clinical notes, but we chose it for the completeness of the dataset. They form the explanation $\mathcal{E}=\{(o,z,d)\}$ for $(R,d^{\star})$ . This annotation process was carried out by 9 clinical physicians and subsequently verified for accuracy and completeness by three senior medical experts. Table 2: Statistics of DiReCT. | Medical domain | # cat. | # samples | $|\mathcal{D}_{i}|$ | $|\mathcal{D}^{\star}_{i}|$ | $|\mathcal{O}|$ | Length | | --- | --- | --- | --- | --- | --- | --- | | Cardiology | 7 | 184 | 27 | 16 | 8.7 | 1156.6 t | | Gastroenterology | 4 | 103 | 11 | 7 | 4.3 | 1026.0 t | | Neurology | 5 | 77 | 17 | 11 | 11.9 | 1186.3 t | | Pulmonology | 5 | 92 | 26 | 17 | 10.7 | 940.7 t | | Endocrinology | 4 | 55 | 20 | 14 | 6.9 | 1063.5 t | | Overall | 25 | 511 | 101 | 65 | 8.5 | 1074.6 t | Table 2 summarizes statistics of our dataset. The second and third columns (“# cats.” and “# samples”) show the numbers of disease categories and samples in the respective medical domains. $|\mathcal{D}_{i}|$ and $|\mathcal{D}_{i}^{\star}|$ are the total numbers of diagnoses (diseases) and PDDs, summed over all diagnostic categories in the medical domain, respectively. $|\mathcal{O}|$ is the average number of annotated observations. “Length” is the average number of tokens in $R$ . <details> <summary>x3.png Details</summary>  ### Visual Description ## Clinical Note to Diagnosis Flow Diagram ### Overview The image presents a flow diagram that connects clinical observations from a patient's medical history and physical exam to a final diagnosis of HFmrEF (Heart Failure with mid-range Ejection Fraction). The diagram links information from a "Clinical Note" to a "Rationale" section, ultimately leading to a "Diagnosis." ### Components/Axes * **Clinical Note:** This section contains information gathered from the patient's chief complaint, history of present illness, past medical history, family history, physical exam, and pertinent results. * **Rationale:** This section provides the reasoning and medical knowledge that connects the clinical findings to the diagnosis. * **Diagnosis:** This section presents the final diagnosis based on the clinical note and rationale. The diagnoses are: Suspected HF, Strongly Suspected HF, HF, and HFmrEF. ### Detailed Analysis or ### Content Details **Clinical Note:** * **Chief Complaint:** Scrotal and leg swelling. * **History of Present Illness:** In the last 3 days, the patient's condition has become quite swollen. The swelling is similar to when the patient was admitted with acute CHF. EKG was consistent with priors (NSR, NANI, changes). The left ventricle is mildly enlarged. The patient was given with good UOP. * **Past Medical History:** Diabetes, Hypertension, CKD (stage 3), GERD, Depression, Amputation, Pneumonia, Osteoarthritis, History of Asthma. * **Family History:** There is no family history of artery. * **Physical Exam:** * LUNG: Bibasilar rales that do not clear with deep inspiration. * ABDOMEN: Nondistended, all quadrants. * EXTREMITIES: Bilateral pitting edema to the sacrum, extending to the up abdomen. Warm, well perfused. * HEENT: AT/NC, EOMI, PERRL. * **Pertinent Results:** * 03:50 PM BLOOD: WBC-8.0, RBC-3.26*, Hgb-9.3*, Hct-30.9*, MCHC-29.9*. * 11:30 AM BLOOD: proBNP-3843. Overall left ventricular systolic function is mildly depressed (LVEF = 45-50%) without regional wall motion abnormalities. Imaging suggests an increased filling pressure (PCWP > Hg). **Rationale:** * Peripheral oedema is a sign of heart failure. * Hypertension is the risk factor of heart failure. * NT-proBNP 3843 ≥ 125 pg/ml is a diagnostic criteria of strong HF. * Cardiac structure abnormalities are diagnostic criteria of heart failure. * Cardiac systolic dysfunction ~49% can lead to the diagnosis of HFmrEF. **Diagnosis:** * Suspected HF * Strongly Suspected HF * HF * HFmrEF ### Key Observations * The diagram illustrates a step-by-step process of how clinical information is used to arrive at a diagnosis of HFmrEF. * The rationale section provides the medical justification for each diagnostic step. * The clinical note contains a mix of subjective (patient complaints) and objective (lab results, physical exam findings) data. * The proBNP value of 3843 is significantly higher than 125 pg/ml, supporting the diagnosis of strong HF. * The LVEF of 45-50% falls within the range for HFmrEF. ### Interpretation The diagram effectively demonstrates the clinical reasoning process involved in diagnosing heart failure. It highlights the importance of integrating patient history, physical exam findings, and laboratory results to arrive at an accurate diagnosis. The flow from "Clinical Note" to "Rationale" to "Diagnosis" provides a clear and logical framework for understanding the diagnostic process. The diagram emphasizes the role of specific clinical findings, such as peripheral edema, hypertension, elevated proBNP, and reduced LVEF, in supporting the diagnosis of HFmrEF. The diagram suggests a systematic approach to diagnosis, where each piece of evidence contributes to the final conclusion. </details> Figure 3: An annotation sample of Heart Failure (HF). The left part is the clinical note alongside extracted observations by a doctor. The middle part outlines the steps of the rationale for the premise corresponding to each diagnostic node shown in the right part. 3.4 Task Definition We propose two tasks with different levels of supplied external knowledge. The first task is, given $R$ and $\mathcal{G}$ , to predict the associated PDD $d^{\star}$ and generate an explanation $\mathcal{E}$ that explains the model’s diagnostic procedure from $R$ to $d^{\star}$ , i.e., letting $M$ denote a model: $$ \displaystyle\hat{d}^{\star},\hat{\mathcal{E}}=M(R,\mathcal{G}), \tag{1} $$ where $\hat{d}^{\star}∈\cup_{i}\mathcal{D}^{\star}_{i}$ and $\hat{\mathcal{E}}$ are predictions for the PDD and explanation, respectively. With this task, the knowledge of specific diagnostic procedures in existing guidelines can be used for prediction, facilitating interpretability. The second task takes $\mathcal{K}$ as input instead of $\mathcal{G}$ , i.e.,: $$ \displaystyle\hat{d}^{\star},\hat{\mathcal{E}}=M(R,\mathcal{K}). \tag{2} $$ This task allows for the use of broader knowledge of premises for prediction. One may also try a task without any external knowledge. 3.5 Evaluation Metrics We designed three metrics to quantify the predictive performance over our benchmark. (1) Accuracy of diagnosis $\textit{Acc}^{\text{diag}}$ evaluates if a model can correctly identify the diagnosis. $\textit{Acc}^{\text{diag}}=1$ if $d^{\star}=\hat{d}$ , and $\textit{Acc}^{\text{diag}}=0$ otherwise. The average is reported. (2) Completeness of observations $\textit{Obs}^{\text{comp}}$ evaluates whether a model extracts all and only necessary observations for the prediction. Let $\mathcal{O}$ and $\hat{\mathcal{O}}$ denote the sets of observations in $\mathcal{E}$ and $\hat{\mathcal{E}}$ , respectively. The metric is defined as $\textit{Obs}^{\text{comp}}=|\mathcal{O}\cap\hat{\mathcal{O}}|/|\mathcal{O}\cup% \hat{\mathcal{O}}|$ , where the numerator is the number of observations that are common in both $\mathcal{O}$ and $\hat{\mathcal{O}}$ . We find the common observations with an LLM (refer to the supplementary material for more detail). This metric simultaneously evaluates the correctness of each observation and the coverage. To supplement it, we also report the precision $\textit{Obs}^{\text{pre}}$ and recall $\textit{Obs}^{\text{rec}}$ , given by $\textit{Obs}^{\text{pre}}=|\mathcal{O}\cap\hat{\mathcal{O}}|/|\hat{\mathcal{O}}|$ and $\textit{Obs}^{\text{rec}}=|\mathcal{O}\cap\hat{\mathcal{O}}|/|\mathcal{O}|$ . (3) Faithfulness of explanations Faith evaluates if the diagnostic flow toward the PDD is fully supported by observations with faithful rationalizations. This involves establishing a one-to-one correspondence between deductions in the prediction and the ground truth. We use the correspondences established for computing $\textit{Obs}^{\text{comp}}$ . Let $o∈\mathcal{O}$ and $\hat{o}∈\hat{\mathcal{O}}$ denote corresponding observations. This correspondence is considered successful if $z$ and $\hat{z}$ as well as $d$ and $\hat{d}$ associated with $o$ and $\hat{o}$ matches. Let $m(\mathcal{E},\hat{\mathcal{E}})$ denote the number of successful matches. We use the ratio of $m(\mathcal{E},\hat{\mathcal{E}})$ to $|\mathcal{O}\cap\hat{\mathcal{O}}|$ and $|\mathcal{O}\cup\hat{\mathcal{O}}|$ as evaluation metrics $\textit{Exp}^{\text{com}}$ and $\textit{Exp}^{\text{all}}$ , respectively, to see failures come from observations or explanations and diagnosis. 4 Baseline <details> <summary>x4.png Details</summary>  ### Visual Description ## Diagram: Clinical Reasoning Process ### Overview The image illustrates a clinical reasoning process, starting from a clinical note and progressing through perception, observations, diagnostic knowledge graph (KG), and reasoning steps to arrive at a rationale. The process involves iterative reasoning and feedback loops. ### Components/Axes * **Clinical Note:** Represents the initial patient information, depicted as a clipboard icon. * **Narrowing-down:** A green rectangular box indicating the process of filtering information. * **Perception:** A purple rectangular box representing the interpretation of clinical data. * **Observations:** A white rectangular box listing clinical findings. * Numbered list of observations: 1. Elevated blood pressures 2. CXR showed mild pulmonary edema 3. CHF/Cardiomyopathy 4. Severe LV diastolic dysfunction 5. BPs: 148/98, 156/93 * **Diagnostic KG:** A blue rectangular box containing a knowledge graph with nodes labeled a1, a2, a3, a4, and a5. The nodes are interconnected with black and red arrows. * **Reasoning:** Gray rectangular boxes representing reasoning steps, each associated with a gear icon. * **Rationale:** White rectangular boxes showing the reasoning process. Each box contains nodes labeled 1 through 5, and nodes a1, a2, and a4. * **Input Variables (r1-r6):** * r1: Chief Complaint * r2: History of Present Illness * r3: Past Medical History * r4: Family History * r5: Physical Examination * r6: Pertinent Results ### Detailed Analysis or Content Details 1. **Flow from Clinical Note:** The process begins with the "Clinical Note," which feeds into both "Narrowing-down" and "Perception." 2. **Narrowing-down to Perception:** "Narrowing-down" leads to "Perception" via an arrow labeled 'i'. 3. **Perception to Observations:** "Perception" leads to "Observations." 4. **Diagnostic KG Connection:** The "Diagnostic KG" provides input to "Perception" via a teal arrow. 5. **Observations to Reasoning:** "Observations" feed into the "Reasoning" blocks. 6. **Reasoning Iteration:** There is a feedback loop (represented by a curved arrow) between the two "Reasoning" blocks. 7. **Diagnostic KG to Reasoning:** The "Diagnostic KG" also feeds into the "Reasoning" blocks via teal arrows. 8. **Reasoning to Rationale:** Each "Reasoning" block leads to a "Rationale" block. 9. **Rationale Details:** * The first "Rationale" box shows nodes 1-5 connected to node a1 with dashed lines. * The second "Rationale" box shows nodes 1-5 connected to node a1 with dashed lines, and a red arrow from a1 to a2. * The third "Rationale" box shows nodes 1-5 connected to node a1 with dashed lines, a red arrow from a1 to a2, and a red arrow from a2 to a4. 10. **Diagnostic KG Details:** * Node a1 has connections to multiple unlabeled nodes. * Node a2 is connected to a1 (red arrow), a3 (red arrow), and a4 (red arrow). * Node a3 is connected to a2 (red arrow) and a5 (red arrow). * Node a4 is connected to a2 (red arrow) and other unlabeled nodes. * Node a5 is connected to a3 (red arrow). ### Key Observations * The diagram illustrates an iterative process of clinical reasoning. * The "Diagnostic KG" and "Observations" serve as key inputs for the "Reasoning" process. * The feedback loop between the "Reasoning" blocks suggests an iterative refinement of the diagnosis. ### Interpretation The diagram represents a model for clinical decision-making. It starts with initial clinical data, which is then filtered and interpreted. Observations are made based on this perception, and a diagnostic knowledge graph is consulted. Reasoning steps are performed, potentially iteratively, to arrive at a rationale or diagnosis. The feedback loop highlights the dynamic nature of clinical reasoning, where initial assessments can be revised based on new information or insights. The connections between the Diagnostic KG and the Reasoning blocks suggest that prior knowledge and established relationships between medical concepts play a crucial role in the diagnostic process. </details> Figure 4: Pipeline of our baseline. The dotted line in the right-most boxes means deductions from an observation to a diagnosis. Figure 4 shows an overview of our baseline with three LLM-based modules narrowing-down, perception, and reasoning (refer to the supplementary material for more details). The narrowing-down module $U$ takes $R$ as input to make a prediction $\hat{i}$ of the disease category, i.e., $\hat{i}=U(R)$ . Let $d_{t}∈\mathcal{D}_{\hat{i}}$ be the diagnosis that has been reached with $t$ iterations over $\mathcal{K}_{\hat{i}}$ , where $t$ corresponds to the depth of node $d_{t}$ and so is less than or equal to the depth of $\mathcal{K}_{i}$ . $d_{0}$ is the root node of $\mathcal{K}_{\hat{i}}$ . For $d_{0}$ , we apply the perception module to extract all observations in $R$ and explanation $\mathcal{E}_{0}$ to support $d_{0}$ as $$ \displaystyle\hat{\mathcal{O}},\hat{\mathcal{E}_{0}}=W(d_{0},\mathcal{K}_{\hat% {i}}). \tag{3} $$ $\mathcal{K}_{\hat{i}}$ is supplied to facilitate the model to extract all observations for the following reasoning process. We used only pairs of an observation and a premise. We abuse $\mathcal{K}$ to mean this for notation simplicity. Diagnosis $d_{t}$ identifies the set $\{d_{n}\}_{n}$ of its children and so the set $\mathcal{P}_{\hat{i}}(\{d_{n}\}_{n})=\{p∈\mathcal{P}_{i}|(p,d_{n})∈% \mathcal{S}_{i},d_{n}∈\{d_{n}\}\}$ of premises that support $d_{n}$ . Therefore, our reasoning module $V$ iteratively and greedily identifies the next step’s diagnosis (i.e., $d_{t+1}$ ) from $\{d_{n}\}_{n}$ , making a rationalization for each deduction. That is, $V$ verifies whether there exist $o$ ’s in $\hat{\mathcal{O}}$ that supports one $d_{n}$ . If $d_{n}$ is fully supported, $d_{n}$ is identified as $d_{t+1}$ for the $(t+1)$ -th iteration, i.e., $$ \displaystyle d_{t+1},\hat{\mathcal{E}}_{t+1}=V(\hat{\mathcal{O}},\{d_{n}\},% \mathcal{P}_{\hat{i}}(\{d_{n}\}_{n})). \tag{4} $$ Otherwise, the reasoning module fails. $V$ is repeated until $d_{t^{\prime}}$ in $\mathcal{D}^{\star}_{\hat{i}}$ is found or it fails. In our annotation, each observation contributes to deducing only one $d_{t}$ . Therefore, if an observation in $\hat{\mathcal{E}}_{t+1}$ is included in the preceding sets of explanations $\hat{\mathcal{E}}_{0}$ to $\hat{\mathcal{E}}_{t}$ , the corresponding explanation in the preceding sets is removed. 5 Experiments 5.1 Experimental Setup We assess the reasoning capabilities of 7 recent LLMs from diverse families and model sizes, including 5 instruction-tuned models that are openly accessible: LLama3 8B and 70B [AI@Meta, 2024], Zephyr 7B [Tunstall et al., 2023], Mistral 7B [Jiang et al., 2023], and Mixtral 8 $×$ 7B [Jiang et al., 2023]. We have also obtained access to private versions of the GPT-3.5 turbo [OpenAI, 2023b] and GPT-4 turbo [OpenAI, 2023a] These two models are housed on a HIPPA-compliant instance within Microsoft Azure AI Studio. No data is transferred to either Microsoft or OpenAI. This secure environment enables us to safely conduct experiments with the MIMIC-IV dataset, in compliance with the Data Use Agreement., which are high-performance closed-source models. Each LLM is utilized to implement our baseline’s narrowing-down, perception, and reasoning modules. The temperature is set to 0. For computing evaluation metrics, we use LLama3 8B with few-shot prompts to make correspondences between $\mathcal{O}$ and $\hat{\mathcal{O}}$ as well as to verify a match between predicted and ground-truth explanations (refer to the supplementary material for more details). 5.2 Results Comparison among LLMs. Table 3 shows the performance of our baseline built on top of various LLMs. We first evaluate a variant of our task that takes graph $\mathcal{G}=\{\mathcal{G}_{i}\}$ consisting of only procedural flow as external knowledge instead of $\mathcal{K}$ . Comparison between $\mathcal{G}$ and $\mathcal{K}$ demonstrates the importance of supplying premises with the model and LLMs’ capability to make use of extensive external knowledge that may be superficially different from statements in $R$ . Subsequently, some models are evaluated with our task using $\mathcal{K}$ . In addition to the metrics in Section 3.5, we also adopt the accuracy of disease category $\textit{Acc}^{\text{cat}}$ , which gives 1 when $\hat{i}=i$ , as our baseline’s performance depends on it. Table 3: Diagnostic reasoning ability of different LLMs under the proposed baseline method. | | | Diagnosis | Observation | Explanation | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Task | Models | Acc ${}^{\text{cat}}$ | Acc ${}^{\text{diag}}$ | $\textit{Obs}^{\text{pre}}$ | $\textit{Obs}^{\text{rec}}$ | $\textit{Obs}^{\text{comp}}$ | $\textit{Exp}^{\text{com}}$ | $\textit{Exp}^{\text{all}}$ | | With $\mathcal{G}$ | Zephyr 7B | 0.274 | 0.151 | 0.123 ${}_{±\text{0.200}}$ | 0.115 ${}_{±\text{0.166}}$ | 0.092 ${}_{±\text{0.108}}$ | 0.071 ${}_{±\text{0.139}}$ | 0.014 ${}_{±\text{0.037}}$ | | Mistral 7B | 0.507 | 0.306 | 0.211 ${}_{±\text{0.190}}$ | 0.317 ${}_{±\text{0.253}}$ | 0.173 ${}_{±\text{0.157}}$ | 0.230 ${}_{±\text{0.312}}$ | 0.062 ${}_{±\text{0.088}}$ | | | Mixtral 8 $×$ 7B | 0.413 | 0.237 | 0.147 ${}_{±\text{0.165}}$ | 0.266 ${}_{±\text{0.261}}$ | 0.124 ${}_{±\text{0.138}}$ | 0.144 ${}_{±\text{0.268}}$ | 0.029 ${}_{±\text{0.056}}$ | | | LLama3 8B | 0.576 | 0.321 | 0.253 ${}_{±\text{0.156}}$ | 0.437 ${}_{±\text{0.207}}$ | 0.219 ${}_{±\text{0.137}}$ | 0.232 ${}_{±\text{0.316}}$ | 0.071 ${}_{±\text{0.093}}$ | | | LLama3 70B | 0.752 | 0.540 | 0.277 ${}_{±\text{0.146}}$ | 0.537 ${}_{±\text{0.192}}$ | 0.256 ${}_{±\text{0.142}}$ | 0.395 ${}_{±\text{0.320}}$ | 0.112 ${}_{±\text{0.110}}$ | | | GPT-3.5 turbo | 0.679 | 0.455 | 0.389 ${}_{±\text{0.212}}$ | 0.351 ${}_{±\text{0.192}}$ | 0.275 ${}_{±\text{0.167}}$ | 0.331 ${}_{±\text{0.366}}$ | 0.103 ${}_{±\text{0.127}}$ | | | GPT-4 turbo | 0.772 | 0.572 | 0.446 ${}_{±\text{0.207}}$ | 0.491 ${}_{±\text{0.180}}$ | 0.371 ${}_{±\text{0.186}}$ | 0.475 ${}_{±\text{0.363}}$ | 0.199 ${}_{±\text{0.181}}$ | | | With $\mathcal{K}$ | LLama3 8B | 0.576 | 0.344 | 0.235 ${}_{±\text{0.162}}$ | 0.394 ${}_{±\text{0.227}}$ | 0.199 ${}_{±\text{0.142}}$ | 0.327 ${}_{±\text{0.375}}$ | 0.087 ${}_{±\text{0.114}}$ | | LLama3 70B | 0.735 | 0.581 | 0.262 ${}_{±\text{0.146}}$ | 0.501 ${}_{±\text{0.208}}$ | 0.236 ${}_{±\text{0.131}}$ | 0.463 ${}_{±\text{0.374}}$ | 0.125 ${}_{±\text{0.117}}$ | | | GPT-3.5 turbo | 0.652 | 0.413 | 0.347 ${}_{±\text{0.241}}$ | 0.279 ${}_{±\text{0.203}}$ | 0.232 ${}_{±\text{0.184}}$ | 0.374 ${}_{±\text{0.408}}$ | 0.121 ${}_{±\text{0.152}}$ | | | GPT-4 turbo | 0.781 | 0.614 | 0.431 ${}_{±\text{0.207}}$ | 0.458 ${}_{±\text{0.187}}$ | 0.353 ${}_{±\text{0.170}}$ | 0.633 ${}_{±\text{0.338}}$ | 0.247 ${}_{±\text{0.201}}$ | | Table 4: Evaluation of diagnostic reasoning ability of LLMs when no external knowledge is provided. | | | | Observation | Explanation | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | Task | Models | Acc ${}^{\text{diag}}$ | $\textit{Obs}^{\text{pre}}$ | $\textit{Obs}^{\text{rec}}$ | $\textit{Obs}^{\text{comp}}$ | $\textit{Exp}^{\text{com}}$ | $\textit{Exp}^{\text{all}}$ | | With $\mathcal{D}^{\star}$ | LLama3 8B | 0.070 | 0.154 ${}_{±\text{0.139}}$ | 0.330 ${}_{±\text{0.244}}$ | 0.135 ${}_{±\text{0.122}}$ | 0.020 ${}_{±\text{0.100}}$ | 0.004 ${}_{±\text{0.016}}$ | | LLama3 70B | 0.502 | 0.257 ${}_{±\text{0.150}}$ | 0.509 ${}_{±\text{0.213}}$ | 0.237 ${}_{±\text{0.145}}$ | 0.138 ${}_{±\text{0.209}}$ | 0.034 ${}_{±\text{0.054}}$ | | | GPT-3.5 turbo | 0.223 | 0.164 ${}_{±\text{0.242}}$ | 0.149 ${}_{±\text{0.212}}$ | 0.116 ${}_{±\text{0.174}}$ | 0.091 ${}_{±\text{0.231}}$ | 0.025 ${}_{±\text{0.065}}$ | | | GPT-4 turbo | 0.636 | 0.461 ${}_{±\text{0.206}}$ | 0.482 ${}_{±\text{0.160}}$ | 0.378 ${}_{±\text{0.174}}$ | 0.186 ${}_{±\text{0.221}}$ | 0.074 ${}_{±\text{0.090}}$ | | | No Knowledge | LLama3 8B | 0.023 | 0.137 ${}_{±\text{0.159}}$ | 0.258 ${}_{±\text{0.274}}$ | 0.119 ${}_{±\text{0.141}}$ | 0.018 ${}_{±\text{0.083}}$ | 0.006 ${}_{±\text{0.026}}$ | | LLama3 70B | 0.037 | 0.246 ${}_{±\text{0.148}}$ | 0.504 ${}_{±\text{0.222}}$ | 0.227 ${}_{±\text{0.148}}$ | 0.022 ${}_{±\text{0.093}}$ | 0.007 ${}_{±\text{0.030}}$ | | | GPT-3.5 turbo | 0.059 | 0.161 ${}_{±\text{0.238}}$ | 0.148 ${}_{±\text{0.215}}$ | 0.113 ${}_{±\text{0.171}}$ | 0.036 ${}_{±\text{0.131}}$ | 0.011 ${}_{±\text{0.039}}$ | | | GPT-4 turbo | 0.074 | 0.410 ${}_{±\text{0.208}}$ | 0.443 ${}_{±\text{0.191}}$ | 0.324 ${}_{±\text{0.182}}$ | 0.047 ${}_{±\text{0.143}}$ | 0.019 ${}_{±\text{0.058}}$ | | With $\mathcal{G}$ , we can see that GPT-4 achieves the best performance in most metrics, especially related to observations and explanations, surpassing LLama3 70B by a large margin. In terms of accuracy (in both category and diagnosis levels), LLama3 70B is comparable to GPT-4. LLama3 70B also has a higher $\textit{Obs}^{\text{rec}}$ but low $\textit{Obs}^{\text{pre}}$ and $\textit{Obs}^{\text{comp}}$ , which means that this model tends to extract many observations. Models with high diagnostic accuracy are not necessarily excel in finding essential information in long text (i.e., observations) and generating reasons (i.e., explanations). When $\mathcal{K}$ is given, all models show better diagnostic accuracy (except GPT-3.5) and explanations, while observations are slightly degraded. GPT-4 with $\mathcal{K}$ enhances Acc ${}^{\text{diag}}$ , $\textit{Exp}^{\text{com}}$ , and $\textit{Exp}^{\text{all}}$ scores. This suggests that premises and supporting edges are beneficial for diagnosis and explanation. Lower observational performance may indicate that the models lack the ability to associate premises and text in $R$ , which are often superficially different though semantically consistent. LLMs may undergo inherent challenges for evaluation when no external knowledge is supplied. They may have the knowledge to diagnose but cannot make consistent observations and explanations that our task expects through $\mathcal{K}$ . To explore this, we evaluate two settings: (1) giving $D^{\star}$ and (2) no knowledge is supplied to a model (shown in Table 4). The prompts used for this setup are detailed in the supplementary material. We do not evaluate the accuracy of disease category prediction as it is basically the same as Table 3. We can clearly see that with $\mathcal{D}^{\star}$ , GPT-4’s diagnostic and observational scores are comparable to those of the task with $\mathcal{K}$ , though explanatory performance is much worse. Without any external knowledge, the diagnostic accuracy is also inferior. We understand this comparison is unfair, as the prompts differ. We intend to give a rough idea about the challenge without external knowledge. The deteriorated performance can be attributed to inconsistent wording of diagnosis names, which makes evaluation tough. High observational scores imply that observations in $R$ can be identified without relying on external knowledge. There can be some cues to spot them. <details> <summary>x5.png Details</summary>  ### Visual Description ## Bar Chart: Model Performance by Medical Specialty ### Overview The image presents a series of bar charts comparing the performance of three language models (LLama3, GPT-3.5, and GPT-4) across five medical specialties: Cardiology, Gastroenterology, Neurology, Pulmonology, and Endocrinology. The performance is measured by three metrics: Accuracy (Acc), Completeness (Comp), and Faithfulness (Faith). Each specialty has its own subplot, showing the performance of each model on the three metrics. ### Components/Axes * **X-axis:** Categorical, representing the language models: LLama3, GPT-3.5, and GPT-4. Each model is evaluated within each medical specialty. * **Y-axis:** Numerical, ranging from 0.0 to 1.0, representing the performance score. * **Subplots:** Five subplots, each representing a different medical specialty: Cardiology, Gastroenterology, Neurology, Pulmonology, and Endocrinology. * **Legend:** Located at the top of the image. * Acc: Accuracy (light green) * Comp: Completeness (light orange) * Faith: Faithfulness (light blue) ### Detailed Analysis **Cardiology:** * **Acc (light green):** LLama3 ~0.45, GPT-3.5 ~0.45, GPT-4 ~0.47 * **Comp (light orange):** LLama3 ~0.27, GPT-3.5 ~0.30, GPT-4 ~0.37 * **Faith (light blue):** LLama3 ~0.12, GPT-3.5 ~0.10, GPT-4 ~0.12 **Gastroenterology:** * **Acc (light green):** LLama3 ~0.65, GPT-3.5 ~0.43, GPT-4 ~0.78 * **Comp (light orange):** LLama3 ~0.15, GPT-3.5 ~0.25, GPT-4 ~0.30 * **Faith (light blue):** LLama3 ~0.08, GPT-3.5 ~0.08, GPT-4 ~0.15 **Neurology:** * **Acc (light green):** LLama3 ~0.80, GPT-3.5 ~0.73, GPT-4 ~0.85 * **Comp (light orange):** LLama3 ~0.30, GPT-3.5 ~0.35, GPT-4 ~0.45 * **Faith (light blue):** LLama3 ~0.20, GPT-3.5 ~0.10, GPT-4 ~0.15 **Pulmonology:** * **Acc (light green):** LLama3 ~0.45, GPT-3.5 ~0.35, GPT-4 ~0.70 * **Comp (light orange):** LLama3 ~0.25, GPT-3.5 ~0.35, GPT-4 ~0.30 * **Faith (light blue):** LLama3 ~0.10, GPT-3.5 ~0.10, GPT-4 ~0.20 **Endocrinology:** * **Acc (light green):** LLama3 ~0.40, GPT-3.5 ~0.40, GPT-4 ~0.50 * **Comp (light orange):** LLama3 ~0.28, GPT-3.5 ~0.28, GPT-4 ~0.30 * **Faith (light blue):** LLama3 ~0.10, GPT-3.5 ~0.12, GPT-4 ~0.22 ### Key Observations * **Accuracy:** GPT-4 generally has the highest accuracy across all specialties, with Neurology showing the highest accuracy scores overall. * **Completeness:** Completeness scores are generally lower than accuracy scores across all models and specialties. GPT-4 tends to have slightly higher completeness scores. * **Faithfulness:** Faithfulness scores are the lowest among the three metrics, indicating a potential area for improvement for all models. * **Specialty Variation:** The performance of the models varies significantly across different medical specialties, suggesting that some specialties are more challenging than others. ### Interpretation The bar charts provide a comparative analysis of the performance of LLama3, GPT-3.5, and GPT-4 in different medical domains. GPT-4 generally outperforms the other models in terms of accuracy, completeness, and faithfulness. However, the low faithfulness scores across all models suggest that there is room for improvement in generating reliable and trustworthy information. The variation in performance across specialties highlights the importance of tailoring language models to specific domains to optimize their effectiveness. The data suggests that while GPT-4 is a strong performer, further research and development are needed to improve the faithfulness of language models in medical applications. </details> Figure 5: Performance of LLama3 70B, GPT-3.5, and GPT-4 under different medical domains. We use the task with $\mathcal{G}$ . Performance in individual domains. Figure 5 summarizes the performance of LLama3 70B, GPT-3.5, and GPT-4 across different medical domains, evaluated using Acc ${}^{\text{cat}}$ , $\textit{Obs}^{\text{comp}}$ , and $\textit{Exp}^{\text{all}}$ . Neurology gives the best diagnostic accuracy, where GPT-4 achieved an accuracy of 0.806. LLama3 also performed well (0.786). In terms of $\textit{Obs}^{\text{comp}}$ and $\textit{Exp}^{\text{all}}$ , GPT-4’s results were 0.458 and 0.340, respectively, with the smallest difference between the two scores among all domains. This smaller gap indicates that in Neurology, the common observations in prediction and ground truth lead to the correct diagnoses with faithful rationalizations. However, GPT-4 yields a higher diagnostic accuracy score while a lower explanatory score, suggesting that the observations captured by the model or their rationalizations differ from human doctors. For Cardiology and Endocrinology, the diagnostic accuracy of the models is relatively low (GPT-4 achieved 0.458 and 0.468, respectively). Nevertheless, $\textit{Obs}^{\text{comp}}$ and $\textit{Exp}^{\text{all}}$ are relatively high. Endocrinology results in lower diagnostic accuracy and higher explanatory performance. A smaller gap may imply that in these two domains, successful predictions are associated with observations similar to those of human doctors, and the reasoning process may be analogous. Conversely, in Gastroenterology, higher Acc ${}^{\text{cat}}$ ) is accompanied by lower $\textit{Obs}^{\text{comp}}$ and $\textit{Exp}^{\text{all}}$ (especially for LLama3), potentially indicating a significant divergence in the reasoning process from human doctors. Overall, DiReCT demonstrates that the degree of alignment between the model’s diagnostic reasoning ability and that of human doctors varies across different medical domains. Table 5: Consistency of automated evaluation metrics with human judgments. | Model | Observation | Rationalization | | --- | --- | --- | | LLama3 8B | 0.887 | 0.801 | | GPT-4 turbo | 0.902 | 0.836 | Reliability of automatic evaluation. We randomly pick out 100 samples from DiReCT and their prediction by GPT-4 over the task with $\mathcal{G}$ to assess the consistency of our automated metrics to evaluate the observational and explanatory performance in Section 3.3 to human judgments. Three physicians joined this experiment. For each prediction $\hat{o}∈\hat{\mathcal{O}}$ , they are asked to find a similar observation in ground truth $\mathcal{O}$ . For explanatory metrics, they verify if each prediction $\hat{z}∈\hat{\mathcal{E}}$ for $\hat{o}∈\hat{\mathcal{O}}$ align with ground-truth $z∈\mathcal{E}$ corresponding to $o$ . A prediction and a ground truth are deemed aligned for both assessments if at least two specialists agree. We compare LLama3’s and GPT-4’s judgments to explore if there is a gap between these LLMs. As summarized in Table 5, GPT-4 achieves the best results, with LLama3 8B also displaying a similar performance. From these results, we argue that our automated evaluation metrics are consistent with human judgments, and LLama3 is sufficient for this evaluation, allowing the cost-efficient option. A prediction example. Figure 6 shows a sample generated by GPT-4. The ground-truth PDD of the input clinical note is Hemorrhagic Stroke. In this figure, purple, orange, and red indicate explanations only in the ground truth, only in prediction, and common in both, respectively; therefore, red is a successful prediction of an explanation, while purple and orange are a false negative and false positive. GPT-4 treats the observation of aurosis fugax as the criteria for diagnosing Ischemic Stroke. However, this observation only supports Suspected Stroke. Conversely, observation thalamic hematoma, which is the key indicator of Hemorrhagic Stroke, is regarded as a less important clue. Such observation-diagnosis correspondence errors lead to the model’s misdiagnosis. More samples are available in the supplementary material. <details> <summary>x6.png Details</summary>  ### Visual Description ## Clinical Reasoning Diagram: Stroke Diagnosis ### Overview The image presents a clinical reasoning diagram that connects a clinical note with potential diagnoses of stroke (hemorrhagic or ischemic). It links findings from the clinical note to rationales, which then lead to a suspected stroke diagnosis. The diagram uses arrows to show the relationships between the clinical observations, the rationale behind them, and the final diagnosis. ### Components/Axes * **Clinical Note:** A block of text containing patient history, physical exam findings, and pertinent results from a CT scan. * **Rationale:** A series of text boxes explaining the medical reasoning behind linking clinical findings to potential diagnoses. * **Diagnosis:** Two possible diagnoses: Hemorrhagic Stroke and Ischemic Stroke. * **Suspected Stroke:** An intermediate diagnosis node. * **Arrows:** Purple and orange arrows indicate the flow of reasoning from clinical findings to rationales and then to diagnoses. Red arrows also connect clinical findings to rationales. ### Detailed Analysis or ### Content Details **Clinical Note:** * **Present Illness:** The patient underwent a right carotid procedure and per notes, it was uneventful. This was done as an elective procedure after he had episodes of amaurosis fugax. * **Past Medical History:** +HTN (Hypertension), +Diverticulosis, +CHF (Congestive Heart Failure). * **Physical Exam:** Mental status: Awake, doesn't verbalize. Can only say words. Comprehension is relatively spared, can answer with to yes and No type questions. * **Pertinent Results:** CT HEAD W/O CONTRAST. FINDINGS: A cm left thalamic hematoma appears stable when compared to from outside the imaged approximately ago. There is an increased amount of layering hemorrhage in the of the left lateral ventricle. A small amount of intraventricular blood is noted in the of the right lateral ventricle. There is surrounding , which appears from prior CT. **Rationale:** * **Transient vision loss typically indicates a transient ischemic attack, often associated with carotid artery disease.** (Connected to "episodes of amaurosis fugax" with an orange arrow) * **Carotid artery stenosis is an important cause of insufficient blood flow to the brain and is associated with risk of stroke.** (Connected to "carotid stenosis" with a purple arrow) * **CHF reduced ability of the heart to pump blood may lead to increase the risk of stroke.** (Connected to "+CHF" with a red arrow) * **The presence of a thalamic hematoma is directly related to symptoms of stroke, indicating brain bleeding which can lead to stroke.** (Connected to "thalamic hematoma" and "layering hemorrhage" with orange arrows) * **Thalamus hematoma means brain bleeding which is a common diagnostic criterion for hemorrhagic stroke.** (Connected to "thalamic hematoma" with a purple arrow) **Diagnosis:** * **Suspected Stroke:** A central node that both Hemorrhagic Stroke and Ischemic Stroke connect to. * **Hemorrhagic Stroke:** One of the two possible diagnoses. * **Ischemic Stroke:** The other possible diagnosis. **Connections:** * "episodes of amaurosis fugax" (Clinical Note) -> "Transient vision loss typically indicates a transient ischemic attack, often associated with carotid artery disease." (Rationale) -> "Suspected Stroke" (Diagnosis) - Orange Arrows * "carotid stenosis" (Clinical Note) -> "Carotid artery stenosis is an important cause of insufficient blood flow to the brain and is associated with risk of stroke." (Rationale) -> "Suspected Stroke" (Diagnosis) - Purple Arrows * "+CHF" (Clinical Note) -> "CHF reduced ability of the heart to pump blood may lead to increase the risk of stroke." (Rationale) -> "Suspected Stroke" (Diagnosis) - Red Arrows * "thalamic hematoma" and "layering hemorrhage" (Clinical Note) -> "The presence of a thalamic hematoma is directly related to symptoms of stroke, indicating brain bleeding which can lead to stroke." (Rationale) -> "Suspected Stroke" (Diagnosis) - Orange Arrows * "thalamic hematoma" (Clinical Note) -> "Thalamus hematoma means brain bleeding which is a common diagnostic criterion for hemorrhagic stroke." (Rationale) -> "Hemorrhagic Stroke" (Diagnosis) - Purple Arrows ### Key Observations * The diagram connects clinical findings to potential stroke diagnoses through a series of rationales. * The presence of a thalamic hematoma is strongly linked to a diagnosis of hemorrhagic stroke. * Episodes of amaurosis fugax and carotid stenosis are linked to a general "Suspected Stroke" diagnosis, which could be either hemorrhagic or ischemic. * CHF is also linked to a general "Suspected Stroke" diagnosis. ### Interpretation The diagram illustrates the clinical reasoning process in diagnosing a stroke. It shows how specific findings from a patient's history, physical exam, and CT scan can be used to arrive at a diagnosis. The presence of a thalamic hematoma is a key indicator of hemorrhagic stroke, while other factors like amaurosis fugax, carotid stenosis, and CHF contribute to a general suspicion of stroke, which could be either hemorrhagic or ischemic. The diagram highlights the importance of considering multiple factors and their relationships when making a clinical diagnosis. The diagram is useful for understanding the thought process behind diagnosing a stroke and how different clinical findings can point to different types of stroke. </details> Figure 6: An example prediction for a clinical note with PDD of Hemorrhagic Stroke by GPT-4. 6 Conclusion and Limitations We proposed DiReCT as the first benchmark for evaluating the diagnostic reasoning ability of LLMs with interpretability by supplying external knowledge as a graph. Our evaluations reveal a notable disparity between current leading-edge LLMs and human experts, underscoring the urgent need for AI models that can perform reliable and interpretable reasoning in clinical environments. DiReCT can be easily extended to more challenging settings by removing the knowledge graph from the input, facilitating evaluations of future LLMs. Limitations. DiReCT encompasses only a subset of disease categories and considers only one PDD, omitting the inter-diagnostic relationships due to their complexity—a significant challenge even for human doctors. Additionally, our baseline may not use optimal prompts, chain-of-thought reasoning, or address issues related to hallucinations in task responses. Our dataset is solely intended for model evaluation but not for use in clinical environments. The use of the diagnostic knowledge graph is also limited to serving merely as a part of input. Future work will focus on constructing a more comprehensive disease dataset and developing an extensive diagnostic knowledge graph. Acknowledgments and Disclosure of Funding This work was supported by World Premier International Research Center Initiative (WPI), MEXT, Japan. This work is also supported by JSPS KAKENHI 24K20795 and Dalian Haichuang Project for Advanced Talents. References - Zhao et al. 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Appendix A Details of DiReCT A.1 Data Statistics Table 6: Disease statistics of DiReCT. | Domains | Categories | # samples | $|\mathcal{D}_{i}|$ | $|\mathcal{D}^{\star}_{i}|$ | References | | --- | --- | --- | --- | --- | --- | | Cardiology | Acute Coronary Syndromes | 65 | 6 | 3 | [Byrne et al., 2024] | | Aortic Dissection | 14 | 3 | 2 | [Members et al., 2022] | | | Atrial Fibrillation | 10 | 3 | 2 | [Joglar et al., 2024] | | | Cardiomyopathy | 9 | 5 | 4 | [Ommen et al., 2020] | | | Heart Failure | 52 | 6 | 3 | [Heidenreich et al., 2022] | | | Hyperlipidemia | 2 | 2 | 1 | [Su et al., 2021] | | | Hypertension | 32 | 2 | 1 | [Unger et al., 2020] | | | Gastroenterology | Gastritis | 27 | 5 | 3 | [Shah et al., 2021] | | Gastroesophageal Reflux Disease | 41 | 2 | 1 | [Gyawali et al., 2024] | | | Peptic Ulcer Disease | 28 | 3 | 2 | [Kavitt et al., 2019] | | | Upper Gastrointestinal Bleeding | 7 | 2 | 1 | [Barkun et al., 2019] | | | Neurology | Alzheimer | 10 | 2 | 1 | [McKhann et al., 1984] | | Epilepsy | 8 | 3 | 2 | [Igaku-Shoin-Ltd., 2018] | | | Migraine | 4 | 3 | 2 | [Lipton et al., 2001] | | | Multiple Sclerosis | 27 | 6 | 4 | [Lublin, 2005] | | | Stroke | 28 | 3 | 2 | [Kleindorfer et al., 2021] | | | Pulmonology | Asthma | 13 | 7 | 5 | [Qaseem et al., 2011] | | COPD | 19 | 6 | 4 | [Gupta et al., 2013] | | | Pneumonia | 20 | 4 | 2 | [Olson and Davis, 2020] | | | Pulmonary Embolism | 35 | 5 | 3 | [Konstantinides et al., 2020] | | | Tuberculosis | 5 | 3 | 2 | [Lewinsohn et al., 2017] | | | Endocrinology | Adrenal Insufficiency | 20 | 4 | 3 | [Charmandari et al., 2014] | | Diabetes | 13 | 4 | 2 | [ElSayed et al., 2023] | | | Pituitary | 12 | 4 | 3 | [Tritos and Miller, 2023] | | | Thyroid Disease | 10 | 6 | 4 | [AlexanderErik et al., 2017] | | Table 6 provides a detailed breakdown of the disease categories included in DiReCT. The column labeled # samples indicates the number of data points. The symbols $|\mathcal{D}_{i}|$ and $|\mathcal{D}^{\star}_{i}|$ denote the total number of diagnoses (diseases) and PDDs, respectively. Existing guidelines for diagnosing diseases were used as References, forming the foundation for constructing the diagnostic knowledge graphs. As some premise may not included in the referred guidelines. During annotation, physicians will incorporate their own knowledge to complete the knowledge graph. A.2 Structure of Knowledge Graph The entire knowledge graph, denoted as $\mathcal{K}$ , is stored in separate JSON files, each corresponding to a specific disease category $i$ as $\mathcal{K}_{i}$ . Each $\mathcal{K}_{i}$ comprises a procedural graph $\mathcal{G}_{i}$ and the corresponding premise $p$ for each disease. As illustrated in Figure 7, the procedural graph $\mathcal{G}_{i}$ is stored under the key "Diagnostic" in a dictionary structure. A key with an empty list as its value indicates a leaf diagnostic node as $d^{\star}$ . The premise for each disease is saved under the key of "Knowledge" with the corresponding disease name as an index. For all the root nodes (e.g., Suspected Heart Failure), we further divide the premise into "Risk Factors", "Symptoms", and "Signs". Note that each premise is separated by ";". <details> <summary>x7.png Details</summary>  ### Visual Description ## Diagnostic and Knowledge Data Structure ### Overview The image presents a structured data representation, likely in JSON format, containing diagnostic criteria and knowledge related to "Suspected Heart Failure." It outlines risk factors, symptoms, signs, and specific diagnostic thresholds for different types of heart failure. ### Components/Axes The data is organized into two main sections: "Diagnostic" and "Knowledge." * **Diagnostic**: Contains nested categories for "Suspected Heart Failure," "Strongly Suspected Heart Failure," and "Heart Failure." The "Heart Failure" category further breaks down into "HFrEF," "HFmrEF," and "HFpEF." * **Knowledge**: Contains information about "Suspected Heart Failure," including "Risk Factors," "Symptoms," and "Signs." It also includes criteria for "Strongly Suspected Heart Failure" and "Heart Failure," as well as definitions for "HFrEF," "HFmrEF," and "HFpEF." ### Detailed Analysis or Content Details **Diagnostic Section:** * **Suspected Heart Failure**: * **Strongly Suspected Heart Failure**: * **Heart Failure**: * **HFrEF**: Empty list `[]` * **HFmrEF**: Empty list `[]` * **HFpEF**: Empty list `[]` **Knowledge Section:** * **Suspected Heart Failure**: * **Risk Factors**: "CAD; Hypertension; Valve disease; Arrhythmias; CMPs; Congenital heart disease; Infective; Drug-induced; Infiltrative, Storage disorders, Endomyocardial disease, Pericardial disease, Metabolic, Neuromuscular disease" * **Symptoms**: "Breathlessness; Orthopnoea; Paroxysmal nocturnal dyspnoea; Reduced exercise tolerance; Fatigue; tiredness; increased time to recover after exercise; Ankle swelling; Nocturnal cough; Wheezing; Bloated feeling; Loss of appetite; Confusion (especially in the elderly); Depression; Palpitation; Dizziness; Syncope" * **Signs**: "Elevated jugular venous pressure; Hepatojugular reflux; Third heart sound (gallop rhythm); Laterally displaced apical impulse; Weight gain (>2 kg/week); Weight loss (in advanced HF); Tissue wasting (cachexia); Cardiac murmur; Peripheral edema (ankle, sacral, scrotal); Pulmonary crepitations; Pleural effusion; Tachycardia; Irregular pulse; Tachypnoea; Cheyne-Stokes respiration; Hepatomegaly; Ascites; Cold extremities; Oliguria; Narrow pulse pressure." * **Strongly Suspected Heart Failure**: "NT-proBNP > 125 pg/mL; BNP > 35 pg/mL" * **Heart Failure**: "Abnormal findings from echocardiography\uff1aLV mass index>95 g/m2 (Female), > 115 g/m2 (Male); Relative wall thickness >0.42; LA volume index>34 mL/m2; E/e ratio at rest >9; PA systolic pressure >35 mmHg; TR velocity at rest >2.8 m/s" * **HFrEF**: "LVEF<40%" * **HFmrEF**: "LVEF41-49%" * **HFpEF**: "LVEF>50%" ### Key Observations * The "Diagnostic" section provides a hierarchical structure for classifying heart failure, but the lists for HFrEF, HFmrEF, and HFpEF are currently empty. * The "Knowledge" section offers a comprehensive list of risk factors, symptoms, and signs associated with suspected heart failure. * Specific diagnostic criteria are provided for "Strongly Suspected Heart Failure" based on NT-proBNP and BNP levels. * Echocardiographic findings are used to define "Heart Failure," including LV mass index, relative wall thickness, LA volume index, E/e ratio, PA systolic pressure, and TR velocity. * The definitions for HFrEF, HFmrEF, and HFpEF are based on Left Ventricular Ejection Fraction (LVEF) percentages. ### Interpretation The data structure appears to be designed for a clinical decision support system or a knowledge base related to heart failure diagnosis and management. The "Diagnostic" section likely serves as a framework for classifying patients based on diagnostic findings, while the "Knowledge" section provides the clinical context and criteria for making those classifications. The empty lists under HFrEF, HFmrEF, and HFpEF in the "Diagnostic" section suggest that this part of the structure is intended to hold specific patient data or further sub-classifications that are not yet populated in this example. The provided criteria for "Strongly Suspected Heart Failure" and "Heart Failure" offer clear thresholds for objective markers, aiding in the diagnostic process. </details> Figure 7: A sample of knowledge graph for Heart Failure. Each premise under the key of "Knowledge" is separated with ";". A.3 Annotation and Tools We have developed proprietary software for annotation purposes. As depicted in Figure 8, annotators are presented with the original text as observations $o$ and are required to provide rationales ( $z$ ) to explain why a particular observation $o$ supports a disease $d$ . The left section of the figure, labeled Input1 to Input6, corresponds to different parts of the clinical note, specifically the chief complaint, history of present illness, past medical history, family history, physical exam, and pertinent results, respectively. Annotators will add the raw text into the first layer by left-clicking and dragging to select the original text, then right-clicking to add it. After each observation, a white box will be used to record the rationales. Finally, a connection will be made from each rationale to a disease, represented in a grey box. The annotation process strictly follow the knowledge graph. Both the final annotation and the raw clinical note will be saved in a JSON file. We provide the code to compile these annotations and detailed instructions for using our tool on GitHub. <details> <summary>extracted/5772958/Images_spp/annotation.png Details</summary>  ### Visual Description ## Medical Diagram: Diabetes Progression ### Overview The image is a medical diagram illustrating the potential progression of diabetes, starting from initial indicators and risk factors (Layer 1) and moving through intermediate stages (Layers 2, 3, and 4) to Type II Diabetes (Layer 5). The diagram uses a layered structure with arrows indicating the flow of information or progression of the condition. ### Components/Axes * **Title:** Medical (top-left) * **Input Area:** Top-left corner, labeled "Input text" and "Input" with buttons "Input1" through "Input6". * **Layers:** The diagram is divided into five layers, labeled "Layer1" through "Layer5" horizontally across the top. * **Nodes:** Each layer contains several nodes representing medical conditions or test results. * **Arrows:** Red arrows connect nodes across layers, indicating relationships or progression. ### Detailed Analysis or ### Content Details **Layer 1:** * **C-peptide release test:** "C-peptide release test hints the peak relate. did not fall back" (Top node, color not specified, assumed to be blue). * **Insulin release test:** "Insulin release test hints the peak relate. did not fall back" (Second node, color not specified, assumed to be blue). * **BLOOD Glucose-298:** "BLOOD Glucose-298" (Third node, color not specified, assumed to be blue). * **CKD:** "CKD" (Fourth node, color not specified, assumed to be blue). * **Progressive short term memory loss:** "progressive short term memory loss" (Fifth node, color not specified, assumed to be blue). * **Hypertension:** "Hypertension" (Sixth node, yellow). * **Dyslipidemia:** "Dyslipidemia" (Seventh node, color not specified, assumed to be yellow). * **Coronary artery disease:** "Coronary artery disease as" (Eighth node, color not specified, assumed to be yellow). **Layer 2:** * **Related C-peptide peak:** "Related C-peptide peak is more common in patients with type" (Top node, color not specified, assumed to be blue). * **Related insulin peak:** "Related insulin peak is more common in patients with type II" (Second node, color not specified, assumed to be blue). * **Abnormal random blood glucose:** "Abnormal random blood glucose is a diagnostic criteria of di" (Third node, color not specified, assumed to be blue). * **CKD (description):** "CKD is a kind of microangiopathy, which is a symptom of diabe" (Fourth node, color not specified, assumed to be blue). * **Progressive short term memory loss (description):** "Progressive short term memory loss is a symptom of diabetes." (Fifth node, color not specified, assumed to be blue). * **Hypertension (description):** "Hypertension is a risk factor of diabetes." (Sixth node, color not specified, assumed to be yellow). * **Dyslipidemia (description):** "Dyslipidemia is a risk factor of diabetes." (Seventh node, color not specified, assumed to be yellow). * **Coronary artery disease (description):** "Coronary artery disease as are risk factors of diabetes." (Eighth node, color not specified, assumed to be yellow). **Layer 3:** * **Suspected Diabetes:** "Suspected Diabetes" (Single node, gray). **Layer 4:** * **Diabetes:** "Diabetes" (Single node, gray). **Layer 5:** * **Type II Diabetes:** "Type II Diabetes" (Single node, gray). **Connections (Arrows):** * Multiple red arrows originate from Layer 1 nodes (CKD, Progressive short term memory loss, Hypertension, Dyslipidemia, Coronary artery disease) and converge on the "Suspected Diabetes" node in Layer 3. * A red arrow connects "Suspected Diabetes" in Layer 3 to "Diabetes" in Layer 4. * A red arrow connects "Diabetes" in Layer 4 to "Type II Diabetes" in Layer 5. **Text Area (Left Side):** * "believe that he is prescribed too many medications, and less in" * "at when he takes all of them, he is morefatigued and less in" * "ite Add to first" * "s pi" * "since his last d" ### Key Observations * The diagram illustrates a progression from risk factors and initial test results to suspected diabetes, diabetes, and finally Type II diabetes. * Several risk factors (Hypertension, Dyslipidemia, Coronary artery disease) directly lead to "Suspected Diabetes." * The "Input" area suggests the possibility of entering patient data to influence the diagram's output. ### Interpretation The diagram represents a simplified model of diabetes progression. It suggests that certain risk factors and initial diagnostic findings can lead to a suspicion of diabetes, which can then progress to a diagnosis of diabetes and ultimately to Type II diabetes. The red arrows indicate a causal or correlational relationship between the nodes. The input area suggests a tool designed to assess diabetes risk based on patient data. The text area on the left seems to be a note or comment, possibly related to patient management or medication. </details> Figure 8: Demonstration of our annotation tool. A.4 Access to DiReCT Implementation code and annotation tool are available through https://github.com/wbw520/DiReCT. Data will be released through PhysioNet due to safety issues according to the license of MIMIC-IV (PhysioNet Credentialed Health Data License 1.5.0). We will use the same license for DiReCT. The download link will be accessible via GitHub. We confirm that this GitHub link and data link are always accessible. We confirm that we will bear all responsibility in case of violation of rights. Appendix B Implementation of Baseline Method B.1 Prompt Settings Table 7: Prompt for narrowing-down module. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will review a clinical ’Note’ and your ’Response’ is to diagnose the disease that the patient have for this admission. | | All possible disease options are in a list structure: {disease_option}. | | Note that you can only choose one disease from the disease options and directly output the origin name of that disease. | | Now, start to complete your task. | | Don’t output any information other than your ’Response’. | | ’Note’: | | {note} | | Your ’Response’: | Table 8: Prompt for perception module. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will review a part of clinical "Note" from a patient. | | The disease for which the patient was admitted to hospital this time is {disease}. | | Your task is to extract the original text as confidence "Observations" that lead to {disease}. | | Here are some premise for the diagnosis of this disease category. You can refer them for your task. Premise are: {premise} | | Note that you also need to briefly provide the "Reason" for your extraction. | | Note that both "Observations" and "Reason" should be string. | | Note that your "Response" should be a list structure as following | | : [["Observation", "Reason"], ……, ["Observation", "Reason"]] | | Note that if you can’t find any "Observation" your "Response" should be: []. | | Now, start to complete your task. | | Note that you should not output any information other than your "Response". | | "Note": | | {note} | | Note that you should not output any information other than your "Response". | | Your "Response": | Table 9: Prompt for reasoning module. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will receive a list of "Observations" from a clinical "Note". These "Observations" are possible support to diagnose {disease}. | | Based on these "Observations", you need to diagnose the "Disease" from the following options: {disease_option}. | | Here are some golden standards to discriminate diseases. You can refer them for your task. Golden standards are: {premise} | | Note that you can only choose one "Disease" from the disease options and directly output the name in disease options. | | Note that you also required to select the "Observations" that satisfy the golden standard to diagnose the "Disease" you choose. | | Note that you also required to provide the "Reason" for your choice. | | Note that your "Response" should be a list structure as following | | :[["Observation", "Reason", "Disease"], ……, ["Observation", "Reason", "Disease"]] | | Note that if you can’t find any "Observation" to support a disease option, your "Response" should be: None | | Now, start to complete your task. | | Note that you should not output any information other than your "Response". | | "Observations": | | {observation} | | Note that you should not output any information other than your "Response". | | Your "Response": | In this section, we demonstrate the prompt we used for each module (From Table 7 - 9 for narrowing-down, perception, and reasoning module, respectively). In Table 7, {disease_option} is the name for all disease categories, and {note} is the content for the whole clinical note. The response for the model is the name of a possible disease $\hat{i}$ . In Table 8, {disease} is the disease category name predicted in narrowing-down. The content marked blue is the premise, which is only provided during the $\mathcal{k}$ setting. In this module, {premise} is offered with all information in the knowledge graph. Different to narrowing-down, {note} is implemented for each clinical data $R=\{r\}$ and the outputs are combined together for $\hat{\mathcal{O}}$ and $\hat{\mathcal{E}}$ . In Table 9, {disease} is the disease category name and {disease_option} is consisted by the children nodes $\{d_{n}\}_{n}$ . Similarly, the premise on the blue is only available for the $\mathcal{k}$ setting. It provides the premise that are criteria for the diagnosis of each children node. {observation} is the extracted $\hat{\mathcal{O}}$ in previous step. We provide all the prompts and the complete implementation code on GitHub. B.2 Details of Automatic Evaluation The automatic evaluation is realized by LLama3 8B. We demonstrate the prompt for this implement in Table 10 (for observation) and Table 11 (for rationalization). Note that we do not use few-shot samples for the evaluation of observation. In Table 10, {gt_observation} and {pred_observation} are from model prediction and ground-truth. As this is a simple similarity comparison task to discriminate whether the model finds similar observations to humans, LLama3 itself have such ability. We do not strict to exactly match due to the difference in length of extracted raw text (as long as the observation expresses the same description). In Table 11, {gt_reasoning} and {pred_reasoning} are from model prediction and ground-truth. We require the rationale to be complete (content of the expression can be understood from the rationale alone) and meaningful; therefore, we provide five samples for this evaluation. We also provide all the prompts and the complete implementation code on GitHub. Table 10: Prompt for evaluation of observation. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will receive two "Observations" extracted from a patient’s clinical note. | | Your task is to discriminate whether they textually description is similar? | | Note that "Response" should be one selection from "Yes" or "No". | | Now, start to complete your task. | | Don’t output any information other than your "Response". | | "Observation 1": {gt_observation} | | "Observation 2": {pred_observation} | | Your "Response": | Table 11: Prompt for evaluation of rationalization. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will receive two "Reasoning" for the explanation of why an observation cause a disease. | | Your task is to discriminate whether they explain a similar medical diagnosis premise? | | Note that "Response" should be one selection from "Yes" or "No". | | Here are some samples: | | Sample 1: | | "Reasoning 1": Facial sagging is a classic symptom of stroke | | "Reasoning 2": Indicates possible facial nerve palsy, a common symptom of stroke | | "Response": Yes | | Sample 2: | | "Reasoning 1": Family history of Diabetes is an important factor | | "Reasoning 2": Patient’s mother had a history of Diabetes, indicating a possible genetic predisposition to stroke | | "Response": Yes | | Sample 3: | | "Reasoning 1": headache is one of the common symptoms of HTN | | "Reasoning 2": Possible symptom of HTN | | "Response": No | | Sample 4: | | "Reasoning 1": Acute bleeding is one of the typical symptoms of hemorrhagic stroke | | "Reasoning 2": The presence of high-density areas on Non-contrast CT Scan is a golden standard for Hemorrhagic Stroke | | "Response": No | | Sample 5: | | "Reasoning 1": Loss of strength on one side of the body, especially when compared to the other side, is a common sign of stroke | | "Reasoning 2": Supports ischemic stroke diagnosis | | "Response": No | | Now, start to complete your task. | | Don’t output any information other than your "Response". | | "Reasoning 1": {gt_reasoning} | | "Reasoning 2": {pred_reasoning} | | Your "Response": | B.3 Prediction Samples Figure 9 and 10 shows two sample generated by GPT-4. The ground-truth PDD of the input clinical note is Gastroesophageal Reflux Disease (GERD) and Heart Failure (HF). In these figure, purple, orange, and red indicate explanations only in the ground truth, only in prediction, and common in both, respectively; therefore, red is a successful prediction of an explanation, while purple and orange are a false negative and false positive. <details> <summary>x8.png Details</summary>  ### Visual Description ## Clinical Diagnosis Diagram: GERD vs. Suspected PBC ### Overview The image presents a clinical diagnostic diagram that differentiates between suspected Primary Biliary Cholangitis (PBC) and Gastroesophageal Reflux Disease (GERD). It connects clinical notes, rationale, and diagnosis through a series of statements and arrows, illustrating the thought process behind diagnosing GERD. ### Components/Axes * **Clinical Note:** Contains patient's chief complaint, present illness, past medical history, and pertinent results from examinations. * **Rationale:** Provides supporting statements and common findings related to GERD. * **Diagnosis:** Indicates the final diagnosis of GERD. * **Nodes:** * "Suspected GERD" - A preliminary diagnostic consideration. * "GERD" - The final diagnosis. * **Connectors:** Arrows linking clinical findings to rationale and ultimately to the diagnosis. * **Color Coding:** * Purple arrows connect clinical findings to the "Suspected GERD" node. * Orange arrows connect clinical findings and rationale to the "GERD" node. * Red arrows connect specific clinical results to the "GERD" node. ### Detailed Analysis or Content Details **Clinical Note (Left Side):** * **Chief Complaint:** epigastric and substernal chest pain * **Present Illness:** suspected PBC with severe epigastric pain that radiates to her mid-sternal area beginning at \*\* AM. She noted gradual 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</details> Figure 9: An example prediction for a clinical note with PDD of GERD by GPT-4 <details> <summary>x9.png Details</summary>  ### Visual Description ## Clinical Reasoning Diagram: Heart Failure Diagnosis ### Overview This image presents a diagram illustrating the clinical reasoning process for diagnosing heart failure (HF). It connects clinical observations from a patient's case (Clinical Note) to the rationale behind diagnostic decisions, ultimately leading to a diagnosis (Diagnosis). The diagram uses arrows to show the flow of information and relationships between different findings and conclusions. ### Components/Axes The diagram is divided into three main sections, arranged horizontally: 1. **Clinical Note:** This section contains excerpts from a patient's medical record, including chief complaints, present history, and pertinent results. 2. **Rationale:** This section provides the medical reasoning and explanations that link the clinical findings to potential diagnoses. 3. **Diagnosis:** This section presents the possible diagnoses based on the clinical findings and rationale. The diagram uses two colors of arrows to connect the sections: * **Orange Arrows:** Connect clinical findings to the rationale. * **Purple Arrows:** Connect the rationale to the diagnosis. ### Detailed Analysis or ### Content Details **1. Clinical Note (Left Section):** * **Chief Complaint:** scrotal and leg swelling. * **Present History:** He was seen *************************** anasarca. At that time, his lasix was increased from *********** to ****** BID. In the ED initial vitals ***************************98% RA. Blood pressure remained 200/90 throughout the ED course. Labs were significant for ********** since \_\_\_ (1.**********->3.2). EKG was consistent with priors (NSR, NANI, no ischemic changes). CXR showed mild pulmonary edema. He was given **********with good UOP. Bedside cardiac ultrasound w/mild effusion no evidence of tamponade physiology. Bedside scrotal **********, no evidence of vascular compromise.... * **Pertinent Results:** 07:10AM BLOOD C3-142 C4-27 proBNP-5145 * The left atrium is moderately dilated. No atrial septal defect is seen **************** Doppler. Overall left ventricular systolic function is mildly depressed (LVEF= 45-50%) without regional wall motion abnormalities. **2. Rationale (Middle Section):** * Swelling in the legs can be a sign of fluid retention, which is a common symptom of heart failure. * Cardiac effusions are often associated with heart failure, indicating fluid overload or heart dysfunction. * Elevated proBNP levels are a biomarker for heart failure, indicating cardiac stress and heart dysfunction. * LVEF in the range of 45-50% suggests preserved or mildly reduced systolic function, aligning with HFpEF. **3. Diagnosis (Right Section):** * Peripheral oedema is a sign of heart failure -> Suspected HF * BNP ≥ 35 pg/mL is a strong value for heart failure -> Strongly Suspected HF * BNP ≥ 35 pg/mL is a strong value for heart failure -> HF * 40≤LVEF < 50% is the criteria for HFmrEF -> HFmrEF * LVEF in the range of 45-50% suggests preserved or mildly reduced systolic function, aligning with HFpEF -> HFpEF ### Key Observations * The diagram connects specific clinical findings (e.g., leg swelling, pulmonary edema, LVEF range) to the rationale behind considering heart failure. * Elevated proBNP levels are used as a biomarker for heart failure. The proBNP value is 5145. * The diagram shows how different LVEF ranges can lead to different types of heart failure diagnoses (HFmrEF, HFpEF). ### Interpretation The diagram illustrates a simplified clinical reasoning process for diagnosing heart failure. It demonstrates how specific clinical findings, combined with medical knowledge and diagnostic criteria, can lead to a diagnosis of heart failure and its subtypes. The diagram highlights the importance of considering multiple factors, such as symptoms, lab results, and echocardiographic findings, in the diagnostic process. The diagram also shows how the same clinical finding (e.g., BNP ≥ 35 pg/mL) can lead to different levels of diagnostic certainty (e.g., "Strongly Suspected HF" vs. "HF"). </details> Figure 10: An example prediction for a clinical note with PDD of HF by GPT-4 In Figure 9, we can observe that GPT-4 can find the key observation for the diagnosis of GERD, which is consistent with human in both observation and rationale. However, it still lacks the ability to identify all observations and establish accurate relationships for diseases. In Figure 10, the model’s predictions do not align well with those of a human doctor. Key observations, such as the relationships between BNP and LVEF, are incorrectly identified, leading to a final misdiagnosis. B.4 Experiments for No Extra Knowledge Table 12: Prompt for $\mathcal{D}^{\star}$ setting. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will review a clinical ’Note’ and your ’Response’ is to diagnose the disease that the patient have for this admission. | | All possible disease options are in a list structure: {disease_options}. | | Note that you can only choose one disease from the disease options and directly output the origin name of that disease. | | Now, start to complete your task. | | Don’t output any information other than your ’Response’. | | ’Note’: | | {note} | | Your ’Response’: | Table 13: Prompt for no knowledge setting. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will review a clinical ’Note’ and your ’Response’ is to diagnose the disease that the patient have for this admission. | | Note that you can only give one disease name and directly output the name of that "Disease". | | Now, start to complete your task. | | Don’t output any information other than your ’Response’. | | ’Note’: | | {note} | | Your ’Response’: | We demonstrate the prompt used for $\mathcal{D}^{\star}$ and no knowledge settings in Table 12 and Table 13, respectively. {note} is the text of whole clinical note and {note} in Table 12 is the name of all leaf node $\mathcal{D}^{\star}$ . B.5 Experimental Settings All experiments are implemented with a temperature value of 0. All close sourced models are implemented in a local server with 4 NVIDIA A100 GPU. Appendix C Failed Attempts on DiReCT In this section, we discuss some unsuccessful attempts during the experiments. Extract observation from the whole clinical note. We try to diagnose the disease and extract observation, and the corresponding rationale using the prompt shown in Table 14. The {note} is offered by the whole content in the clinical note. We find that even though the model can make the correct diagnosis, only a few observations can be extracted (no more than 4), which decreases the completeness and faithfulness. Table 14: Prompt for extracting observation in one step. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will review a clinical ’Note’, and your ’Response’ is to diagnose the disease that the patient has for this admission. | | All possible disease options are in a list structure: {disease_options}. | | Note that you can only choose one disease from the disease options and directly output the origin name of that disease. | | Note that you also need to extract original text as confidence "Observations" that lead to the "Disease" you selected. | | Note that you should extract all necessary "Observation". | | Note that you also need to briefly provide the "Reason" for your extraction. | | Note that both "Observations" and "Reason" should be string. | | Note that your "Response" should be a list structure as following | | :[["Observation", "Reason", "Disease"], ……, ["Observation", "Reason", "Disease"]] | | Now, start to complete your task. | | Don’t output any information other than your ’Response’. | | ’Note’ | | : {note} | | Your ’Response’: | End-to-End prediction. We also try to output the whole reasoning process in one step (without iteration) when given observations. We show our prompt in Table 15. We find that using such a prompt model can not correctly recognize the relation between observation, rationale, and diagnosis. Table 15: Prompt for extracting observation in one step. | Input Prompt | | --- | | Suppose you are one of the greatest AI scientists and medical expert. Let us think step by step. | | You will receive a list of "Observations" from a clinical "Note" for the diagnosis of stroke. | | Here is the diagnostic route of stroke in a tree structure: | | -Suspected Stroke | | -Hemorrhagic Stroke | | -Ischemic Stroke | | Here are some premise for the diagnosis of this disease. You can refer them for your task. Premise are: {premise} | | Based on these "Observations", starting from the root disease, your target is to diagnose one of the leaf disease. | | Note that you also required to provide the "Reason" for your reasoning. | | Note that your "Response" should be a list structure as following | | :[["Observation", "Reason", "Disease"], ……, ["Observation", "Reason", "Disease"]] | | Note that if you can’t find any "Observation" to support a disease option, your "Response" should be: None | | Now, start to complete your task. | | Note that you should not output any information other than your "Response". | | "Observations": | | {observation} | | Note that you should not output any information other than your "Response". | | Your "Response": | Appendix D Ethical Considerations Utilizing real-world EHRs, even in a de-identified form, poses inherent risks to patient privacy. Therefore, it is essential to implement rigorous data protection and privacy measures to safeguard sensitive information, in accordance with regulations such as HIPAA. We strictly adhere to the Data Use Agreement of the MIMIC dataset, ensuring that the data is not shared with any third parties. All experiments are implement on a private server. The access to GPT is also a private version. AI models are susceptible to replicating and even intensifying the biases inherent in their training data. These biases, if not addressed, can have profound implications, particularly in sensitive domains such as healthcare. Unconscious biases in healthcare systems can result in significant disparities in the quality of care and health outcomes among different demographic groups. Therefore, it is imperative to rigorously examine AI models for potential biases and implement robust mechanisms for ongoing monitoring and evaluation. This involves analyzing the model’s performance across various demographic groups, identifying any disparities, and making necessary adjustments to ensure equitable treatment for all. Continual vigilance and proactive measures are essential to mitigate the risk of biased decision-making and to uphold the principles of fairness and justice in AI-driven healthcare solutions.