Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning

Authors

  • Hao-Cheng Kao HTC Research
  • Kai-Fu Tang HTC Research
  • Edward Chang HTC Research

Abstract

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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Published

2018-04-26

How to Cite

Kao, H.-C., Tang, K.-F., & Chang, E. (2018). Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11902

Issue

Section

Main Track: Machine Learning Applications