Abacha Asma Ben, Demner-Fushman Dina
U.S. National Library of Medicine, Bethesda, MD.
AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:117-126. eCollection 2019.
Despite the recent developments in commercial Question Answering (QA) systems, medical QA remains a challenging task. In this paper, we study the factors behind the complexity of consumer health questions and potential improvement tracks. In particular, we study the impact of information source quality and question conciseness through three experiments. First, an evaluation of a QA method based on a Question-Answer collection created from trusted NIH resources, which outperformed the best results of the medical LiveQA challenge with an average score of 0.711. Then, an evaluation of the same approach using paraphrases and summaries of the test questions, which achieved an average score of 1.125. Our results provide an empirical evidence supporting the key role of summarization and reliable information sources in building efficient CHQA systems. The latter finding on restricting information sources is particularly intriguing as it contradicts the popular tendency ofrelying on big data for medical QA.
尽管商业问答（QA）系统最近有所发展，但医学问答仍然是一项具有挑战性的任务。在本文中，我们研究了消费者健康问题复杂性背后的因素以及潜在的改进方向。具体而言，我们通过三个实验研究了信息源质量和问题简洁性的影响。首先，对基于从美国国立卫生研究院（NIH）可靠资源创建的问答集的QA方法进行评估，其平均得分为0.711，超过了医学LiveQA挑战赛的最佳成绩。然后，使用测试问题的释义和摘要对相同方法进行评估，平均得分为1.125。我们的结果提供了实证证据，支持了摘要和可靠信息源在构建高效的消费者健康问答（CHQA）系统中的关键作用。关于限制信息源的后一个发现尤其引人入胜，因为它与医学问答中依赖大数据的流行趋势相矛盾。
