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DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models

Paper · 2024 · Bowen Wang, Jiuyang Chang, Yiming Qian et al.

Summary

LLMs such as GPT-4 score well on medical exam questions, but those benchmarks use short inputs and say little about whether a model's diagnostic reasoning can be inspected. DiReCT, a NeurIPS 2024 Datasets and Benchmarks paper, targets a more realistic task: inferring a diagnosis from a long clinical note while exposing the evidence and logic behind it. The authors selected 511 notes from MIMIC-IV covering 25 disease categories across 5 medical domains. Physicians annotated each note by linking specific observations to intermediate and final diagnoses and writing a rationale for each link, which yields a multi-evidence entailment tree. A diagnostic knowledge graph built from clinical guidelines accompanies the notes, giving models criteria they may not have learned during pretraining. The authors also built a simple AI-agent baseline that uses the graph to break diagnosis into smaller steps. Even leading LLMs fall clearly short of physicians in this setting, often linking observations to the wrong diagnosis or missing evidence. The benchmark therefore measures both reading (finding evidence) and reasoning, the two capacities clinical decision support needs.

Key points

  • Dataset construction: 511 notes are drawn from MIMIC-IV, with an average input length of about 1,075 tokens. Each note keeps six fields from the SOAP subjective and objective sections, and any text revealing the primary discharge diagnosis (PDD) was removed.
  • Knowledge graph: Nodes are premises and diagnoses. "Supporting" edges link premises to the diagnoses they support, and "procedural" edges link one diagnosis to the next. The procedural part of each disease category forms a tree whose leaves are PDDs.
  • Comparison with prior benchmarks: Earlier resources such as MedMCQA, NLI4CT and NEJM CPC give plain-text or multi-hop explanations. DiReCT instead uses entailment-tree explanations over full clinical notes.
  • Error analysis: In GPT-4 examples, the model sometimes undervalued the key evidence, such as a thalamic hematoma pointing to hemorrhagic stroke. In other cases it found correct evidence but not all of it.
  • Limitations and availability: The benchmark covers only a subset of diseases, considers a single PDD per note, and is meant for evaluation only, not clinical use. Code and data are on GitHub (wbw520/DiReCT).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksMedicine · 241 worksHealth Informatics · 184 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Bowen Wang, Jiuyang Chang, Yiming Qian, Guoxin Chen, Junhao Chen, Zhouqiang Jiang, Jiahao Zhang, Yuta Nakashima and Hajime Nagahara. 2024. DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models. arXiv:2408.01933.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2408.01933 · arXiv 2408.01933

@misc{wang2024direct,
  title = {{DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models}},
  author = {Bowen Wang, Jiuyang Chang, Yiming Qian, Guoxin Chen, Junhao Chen, Zhouqiang Jiang, Jiahao Zhang, Yuta Nakashima and Hajime Nagahara},
  year = {2024},
  doi = {10.48550/arxiv.2408.01933},
  eprint = {2408.01933},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2408.01933}
}

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