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Extractive Clinical Question-Answering With Multianswer and Multifocus Questions: Data Set Development and Evaluation

Sungrim Moon1, Huan He1, Heling Jia1

  • 1Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.

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|June 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces RxWhyQA, a new dataset for training AI to answer complex clinical questions with multiple answers or focuses. The dataset enables more realistic development of extractive question-answering (EQA) systems for healthcare.

Keywords:
artificial intelligencedata setdatasetinformation extractionnatural language processingquestion-answering

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Area of Science:

  • Natural Language Processing (NLP)
  • Artificial Intelligence (AI) in Healthcare
  • Clinical Data Analysis

Background:

  • Extractive Question-Answering (EQA) aids in answering patient questions using clinical notes.
  • Existing datasets lack support for multi-answer and multi-focus questions common in clinical settings.
  • Developing AI for realistic clinical EQA requires specialized datasets.

Purpose of the Study:

  • To create a novel dataset for developing and evaluating clinical EQA systems.
  • To address the limitations of existing datasets by incorporating multi-answer and multi-focus question capabilities.
  • To facilitate the creation of AI solutions that handle complex, natural clinical queries.

Main Methods:

  • Leveraged annotated relations from the 2018 National NLP Clinical Challenges corpus.
  • Generated an EQA dataset including 1-to-N, M-to-1, and M-to-N drug-reason relations.
  • Developed and tested a baseline EQA solution on the newly created dataset.

Main Results:

  • The RxWhyQA dataset comprises 96,939 question-answering entries.
  • 25% of answerable questions required multiple answers, and 2% involved multiple drugs.
  • Baseline EQA achieved an F1-score of 0.72, with notable performance differences for multi-answer and multi-drug questions.

Conclusions:

  • The RxWhyQA dataset is suitable for training and evaluating EQA systems for multi-answer and multi-focus questions.
  • Multi-answer EQA presents significant challenges, necessitating further research and investment.
  • The shared dataset promotes research towards more realistic clinical EQA scenarios.