AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

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Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning
Hao-Cheng Kao, Kai-Fu Tang, Edward Y. Chang

Last modified: 2018-04-26


Online symptom checkers have been deployed by sites such as WebMD and Mayo Clinic to identify possible causes and treatments for diseases based on a patient’s symptoms. Symptom checking first assesses a patient by asking a series of questions about their symptoms, then attempts to predict potential diseases. The two design goals of a symptom checker are to achieve high accuracy and intuitive interactions. In this paper we present our context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.

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