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Large Language Models are Few-Shot Clinical Information Extractors

Paper · 2022 · Monica Agrawal, Stefan Hegselmann, Hunter Lang et al.

Summary

Clinical notes contain many variables that never reach the structured fields of electronic health records. Extracting them is difficult because clinical language is full of jargon and irregular syntax, annotation requires medical expertise, and privacy restrictions keep public corpora scarce and small. The authors test whether general-domain LLMs such as GPT-3/InstructGPT can perform clinical extraction in zero- and few-shot settings. They use the models only through an API, with no gradients or log probabilities. Besides classification, they cover tasks with structured outputs: acronym sense disambiguation, evidence extraction from abstracts, coreference, and medication extraction with status and attributes.

Two techniques make this work. Guided prompt design gives the model a one-shot example in the target format and tells it to follow that format. Resolvers are small post-processing functions that map the model's free text into the task's label space. Licensed clinical datasets cannot be sent to external APIs, so the authors re-annotated snippets of the public CASI corpus into three new benchmarks. Resolved GPT-3 clearly outperforms existing zero- and few-shot baselines. This suggests LLM outputs can replace much hand-built domain knowledge and cut engineering effort.

Key points

  • Five tasks were evaluated:
    • clinical sense disambiguation of 41 acronyms, using 18,164 CASI notes and 8,912 MIMIC notes;
    • biomedical evidence extraction on EBM-NLP, using 187 abstracts plus 20 newly annotated ones;
    • linking pronouns to the expressions they refer to (coreference);
    • identifying the status of each medication;
    • identifying the attributes of each medication.
  • Three new CASI-based datasets were created, each with 105 examples:
    • coreference, with one pronoun–antecedent pair per example;
    • medication status, with 340 medication–status pairs;
    • medication attributes, with 313 medications and 533 attributes.
    • All three are released at huggingface.co/datasets/mitclinicalml/clinical-ie.
  • Method details: prompt templates were handcrafted using only 5 validation examples per task. Resolver complexity is reported as lines of code (LOC), a proxy for human effort. The combined system is called Resolved GPT-3 (GPT-3 + R).
  • Distillation: the paper also shows that GPT-3 pseudolabels can be used, via weak supervision, to train a much smaller model.
  • Limitations:
    • it is hard to make the LLM match detailed annotation schemas at the token level;
    • the model tends to produce some answer even when none exists;
    • evaluation was English-only and mostly on CASI, which is not representative of all clinical text.

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

 Institutions

🎓Massachusetts Institute of TechnologyCambridge · United States211 works · 69,000 citations🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States69 works · 30,953 citations🎓University of MünsterMünster · Germany4 works · 854 citations

 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 worksBiochemistry, Genetics and Molecular Biology · 234 worksMolecular Biology · 231 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Few-shot learning · 45 works

Cite

Cite as Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim and David Sontag. 2022. Large Language Models are Few-Shot Clinical Information Extractors. arXiv:2205.12689.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2205.12689 · arXiv 2205.12689

@misc{agrawal2022large,
  title = {{Large Language Models are Few-Shot Clinical Information Extractors}},
  author = {Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim and David Sontag},
  year = {2022},
  doi = {10.48550/arxiv.2205.12689},
  eprint = {2205.12689},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2205.12689}
}

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