Refuel: Exploring sparse features in deep reinforcement learning for fast disease diagnosis

Peng Y.-STang K.-FChang E.YHSUAN-TIEN LIN2021-09-022021-09-02201810495258https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062170858&partnerID=40&md5=255e840e09007767f2abab5f8c2bf8fdhttps://scholars.lib.ntu.edu.tw/handle/123456789/581363This 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. ? 2018 Curran Associates Inc.All rights reserved.Diagnosis; Machine learning; Reinforcement learning; Disease diagnosis; High probability; Reinforcement learning method; Sparse features; Deep learningRefuel: Exploring sparse features in deep reinforcement learning for fast disease diagnosisconference paper2-s2.0-85062170858