Fusion of Supervised Learning and Reinforcement Learning for Dynamic Treatment Recommendation

简介:

Electronic health records (EHR) have provided a great opportunity to exploit personalized health data to optimize clinical decision making and achieve personalized treatment recommendation. In this talk, we explore how AI could help physicians in prescribing medicines for patients with multi-morbidity (i.e., co-occurrence of two or more diseases). Both Supervised Learning (SL) and Reinforcement Learning (RL) have been employed for this purpose, but with their own drawbacks. For instance, SL relies highly on the clinical guideline and doctors personal experience while RL may produce unacceptable medications due to lack of the supervision from doctors. In this talk, we propose a novel SAVER framework by fusing RL and SL, where RL learns the optimal policy and SL gives a regularization to avoid unacceptable risks. Our experiments show that our SAVER framework can provider more accuracy treatment recommendation than the existing methods.

时间:2022-11-29 (Tuesday) 16:30-18:00
地点:中科院数学与系统科学研究院南楼N204 (线下主会场)、厦大经济楼N302(线下分会场)、腾讯会议:37586125504
会议语言:中文
主办单位:中国科学院大学经济与管理学院、中国科学院预测科学研究中心、厦门大学邹至庄经济研究院、NSFC"计量建模与经济政策研究”基础科学中心
承办单位:
专题网站:
联系人信息:许老师,电话:0592-2182991,邮箱:ysxu@xmu.edu.cn

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