REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis

被引：0

作者：

Peng, Yu-Shao ^{[1
]}

Tang, Kai-Fu ^{[1
]}

Lin, Hsuan-Tien ^{[2
]}

Chang, Edward Y. ^{[1
]}

机构：

[1] HTC Res & Healthcare, New Taipei, Taiwan

[2] Natl Taiwan Univ, Dept CSIE, Taipei, Taiwan

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | 2018年 / 31卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes REFUEL, a reinforcement learning method with two techniques: reward shaping and feature rebuilding, to improve the performance of online symptom checking for disease diagnosis. Reward shaping can guide the search of policy towards better directions. Feature rebuilding can guide the agent to learn correlations between features. Together, they can find symptom queries that can yield positive responses from a patient with high probability. Experimental results justify that the two techniques in REFUEL allow the symptom checker to identify the disease more rapidly and accurately.

引用

页数：10

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