Construction of a question-answering program that automatically answers the medical licensing examination

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Abstract

In recent years, the digitization of medical and health data including clinical data, health diagnostic data, medication log data have been made rapidly. One potential application using electronic medical and health information is to develop a system to make a medical diagnosis according to the contents recorded in the electronic medical data and the appropriate patient information. The task of understanding the condition of the patient and making precisely the diagnosis is hard to be automated and requires the high degree of expertise. Toward a final goal to construct a medical diagnostic support system, as its pilot study, we attempt to build a question-answering program that automatically answers the medical licensing examination. The national medical licensing examination is the form of multiple-choice test and contains a wide variety of problems. There is a type of problems to answer the appropriate disease name among multiple choices given the patient information and test results as a problem statement. We aimed to develop the program to answer this type of questions. By the development of such question-answering program that automatically answers the medical licensing examination, we revealed the fundamental issues and essential difficulties in the information processing of the medical data, and finally constructed the foundation for conducting disease diagnosis support with patient information. In this paper, we developed a question-answering program and actually performed the answering for some problems in 107th and 108th out of national medical licensing examination. We carefully examined and analyzed the results and problems that could be answered correctly and problems that were given incorrect answers, and proposed the improvements to build a more accurate program.

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APA

Ito, S., Tanaka, Y., Kano, Y., & Sakakibara, Y. (2017). Construction of a question-answering program that automatically answers the medical licensing examination. Transactions of the Japanese Society for Artificial Intelligence, 32(2). https://doi.org/10.1527/TJSAI.F-AI30GE

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