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REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis
lib:42a11c944bbebb02 (v1.0.0)
Authors: Yu-Shao Peng,Kai-Fu Tang,Hsuan-Tien Lin,Edward Chang
Where published: NeurIPS 2018 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis
This paper proposes REFUEL, a reinforcement learning method with two techniques: {\em reward shaping} and {\em 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 allows the symptom checker to identify the disease more rapidly and accurately.
Where published: NeurIPS 2018 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/7962-refuel-exploring-sparse-features-in-deep-reinforcement-learning-for-fast-disease-diagnosis
This paper proposes REFUEL, a reinforcement learning method with two techniques: {\em reward shaping} and {\em 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 allows the symptom checker to identify the disease more rapidly and accurately.
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