REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis
Abstract
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.
Cite
Text
Peng et al. "REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis." Neural Information Processing Systems, 2018.Markdown
[Peng et al. "REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/peng2018neurips-refuel/)BibTeX
@inproceedings{peng2018neurips-refuel,
title = {{REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis}},
author = {Peng, Yu-Shao and Tang, Kai-Fu and Lin, Hsuan-Tien and Chang, Edward},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {7322-7331},
url = {https://mlanthology.org/neurips/2018/peng2018neurips-refuel/}
}