Medical Exam Question Answering with Large-scale Reading Comprehension

Authors

  • Xiao Zhang Tsinghua University
  • Ji Wu Tsinghua University
  • Zhiyang He Tsinghua University
  • Xien Liu iFlytek
  • Ying Su iFlytek

Keywords:

reading comprehension, question answering, clinical medicine, aided diagnosis

Abstract

Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP.  In this work, we introduce a question-answering task called MedQA to study answering questions in clinical medicine using knowledge in a large-scale document collection. The aim of MedQA is to answer real-world questions with large-scale reading comprehension. We propose our solution SeaReader---a modular end-to-end reading comprehension model based on LSTM networks and dual-path attention architecture. The novel dual-path attention models information flow from two perspectives and has the ability to simultaneously read individual documents and integrate information across multiple documents. In experiments our SeaReader achieved a large increase in accuracy on MedQA over competing models.  Additionally, we develop a series of novel techniques to demonstrate the interpretation of the question answering process in SeaReader.

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Published

2018-04-27

How to Cite

Zhang, X., Wu, J., He, Z., Liu, X., & Su, Y. (2018). Medical Exam Question Answering with Large-scale Reading Comprehension. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11970