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Total 118 results found since Jan 2013.
Improving Anticoagulant Treatment Strategies of Atrial Fibrillation Using Reinforcement Learning
AMIA Annu Symp Proc. 2021 Jan 25;2020:1431-1440. eCollection 2020.ABSTRACTIn this paper, we developed a personalized anticoagulant treatment recommendation model for atrial fibrillation (AF) patients based on reinforcement learning (RL) and evaluated the effectiveness of the model in terms of short-term and long-term outcomes. The data used in our work were baseline and follow-up data of 8,540 AF patients with high risk of stroke, enrolled in the Chinese Atrial Fibrillation Registry (CAFR) study during 2011 to 2018. We found that in 64.98% of patient visits, the anticoagulant treatment recommended by the RL model were conc...
Source: AMIA Annual Symposium Proceedings - May 3, 2021 Category: Bioinformatics Authors: Lei Zuo Xin Du Wei Zhao Chao Jiang Shijun Xia Liu He Rong Liu Ribo Tang Rong Bai Jianzeng Dong Xingzhi Sun Gang Hu Guotong Xie Changsheng Ma Source Type: research
