报告题目：Fusion of Supervised Learning and Reinforcement Learning for Dynamic Treatment Recommendation
报告人：荆炳义 南方科技大学
报告时间：2022年11月29日(周二)，16:30-18:00
讲座地点：中科院数学与系统科学研究院南楼N204；
腾讯会议ID：375 8612 5504
内容摘要
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. 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.
主讲人简介
荆炳义，南方科技大学统计与数据科学系讲席教授。国家特聘专家，国家自然科学奖二等奖, 教育部高等学校自然科学奖二等奖。 美国统计学会会士 (2018)、数理统计学会会士 (2018)、国际统计学会当选会士(2006)。泛华统计协会理事会成员，中国现场统计学会多元分析委员会理事长，并先后分别担任七家国际学术期刊副主编。研究兴趣包括概率统计、计量经济、网络数据、 强化学习、及生物信息等领域，有多项开创性研究和突破性科研成果。在 Annals of Statistics, Annals of Probability, Biometrika, Journal of the American Statistical Association, Journal of Econometrics等顶级期刊发表论文100余篇，论文引超过3700余次。同时与产业界有着密切的交流与合作。
