Background: Extractive question-answering (EQA) is a useful natural language
processing (NLP) application for answering patient-specific questions by
locating answers in their clinical notes. Realistic clinical EQA can have
multiple answers to a single question and multiple focus points in one
question, which are lacking in the existing datasets for development of
artificial intelligence solutions. Objective: Create a dataset for developing
and evaluating clinical EQA systems that can handle natural multi-answer and
multi-focus questions. Methods: We leveraged the annotated relations from the
2018 National NLP Clinical Challenges (n2c2) corpus to generate an EQA dataset.
Specifically, the 1-to-N, M-to-1, and M-to-N drug-reason relations were
included to form the multi-answer and multi-focus QA entries, which represent
more complex and natural challenges in addition to the basic
one-drug-one-reason cases. A baseline solution was developed and tested on the
dataset. Results: The derived RxWhyQA dataset contains 96,939 QA entries. Among
the answerable questions, 25% require multiple answers, and 2% ask about
multiple drugs within one question. There are frequent cues observed around the
answers in the text, and 90% of the drug and reason terms occur within the same
or an adjacent sentence. The baseline EQA solution achieved a best f1-measure
of 0.72 on the entire dataset, and on specific subsets, it was: 0.93 on the
unanswerable questions, 0.48 on single-drug questions versus 0.60 on multi-drug
questions, 0.54 on the single-answer questions versus 0.43 on multi-answer
questions. Discussion: The RxWhyQA dataset can be used to train and evaluate
systems that need to handle multi-answer and multi-focus questions.
Specifically, multi-answer EQA appears to be challenging and therefore warrants
more investment in research.

本文旨在开发一个新的数据集，用于研发和评估临床试验问题回答系统，该系统可以处理自然的多答案和多焦点问题。通过使用 2018 年国家 NLP 临床挑战（n2c2）语料库进行注释关系，包含 1 到 N、M 到 1 和 M 到 N 药品 - 事件关系，形成多答案和多焦点的 QA 项目，可以更好地挖掘围绕答案的线索，培训和评估需要处理多重答案和多重焦点问题的系统。