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 concordant with the actual prescriptions of the clinicians. Model-concordant treatments were associated with less ischemic stroke and systemic embolism (SSE) event compared with non-concordant ones, but no significant difference on the occurrence rate of major bleeding. We also found that higher proportion of model-concordant treatments were associated with lower risk of death. Our approach identified several high-confidence rules, which were interpreted by clinical experts.PMID:33936519 | PMC:PMC8075452

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