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

By Hao-Cheng Kao, Kai-Fu Tang, Edward Y. Chang. 2018

126 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Information Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Baolin Peng
5. Kam-Fai Wong
6. Xuanjing Huang
7. Zhongyu Wei
8. Xipeng Qiu

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “agnose some potential diseases [246]. Tang et al. [273] formulated inquiry and diagnosis policies as an MDP, and adopted DQN to learn to inquire and diagnose based on limited patient data. Kao et al. [274] applied context-aware HRL scheme to improve accuracy of SC over traditional systems making a l”
• Task-oriented Dialogue System for Automatic Diagnosis (2018, background): “In addition, there are two works where deep reinforcement learning is applied for automatic diagnosis (Tang et al., 2016; Kao et al., 2018).”