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DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing.

Yanjun Gao1, Dmitriy Dligach2, Timothy Miller3

  • 1ICU Data Science Lab, Department of Medicine, University of Wisconsin Madison, 1685 Highland Ave, Madison, 53792, WI, USA.

Journal of Biomedical Informatics
|January 27, 2023
PubMed
Summary
This summary is machine-generated.

A new benchmark, Diagnostic Reasoning Benchmarks (Dr.Bench), was developed to evaluate clinical natural language processing models for diagnostic reasoning. This aims to reduce medical errors and cognitive burden in patient care.

Keywords:
Clinical diagnostic decision supportClinical diagnostic reasoningClinical natural language processing benchmarkNatural language processing

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Clinical Natural Language Processing
  • Medical Informatics

Background:

  • Electronic health records (EHR) and AI-driven clinical decision support systems are advancing healthcare.
  • Provider experience is hampered by information overload and cognitive burden, leading to medical errors.
  • Diagnostic errors, often stemming from heuristic judgment biases, are a significant concern in patient care.

Purpose of the Study:

  • To investigate the potential of clinical natural language processing (cNLP) to model human diagnostic reasoning.
  • To develop and evaluate cNLP models that can reduce cognitive burden and medical errors.
  • To introduce a novel benchmark for assessing diagnostic reasoning capabilities in cNLP.

Main Methods:

  • Introduction of Diagnostic Reasoning Benchmarks (Dr.Bench), a suite of six tasks.
  • Utilized ten publicly available datasets covering clinical text understanding, medical knowledge reasoning, and diagnosis generation.
  • Employed a natural language generation framework to evaluate pre-trained language models for diagnostic reasoning.

Main Results:

  • State-of-the-art generative models were fine-tuned and evaluated on Dr.Bench.
  • Domain adaptation pre-training on medical knowledge showed potential for model improvement.
  • Dr.Bench provides a systematic approach for evaluating cNLP models in diagnostic reasoning.

Conclusions:

  • Dr.Bench is the first clinical task suite designed as a natural language generation framework for evaluating diagnostic reasoning in cNLP.
  • The benchmark aims to advance cNLP science for computerized diagnostic decision support, enhancing healthcare efficiency and accuracy.
  • The study encourages reporting of carbon footprint in future research utilizing Dr.Bench.