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Integrated Machine Learning Approaches for Predicting Ischemic Stroke and Thromboembolism in Atrial Fibrillation.
In this study, we used integrated machine learning and data mining approaches to build 2-year TE prediction models for AF from Chinese Atrial Fibrillation Registry data. We first performed data cleansing and imputation on the raw data to generate available dataset. Then a series of feature construction and selection methods were used to identify predictive risk factors, based on which supervised learning methods were applied to build the prediction models. The experimental results show that our approach can achieve higher prediction performance (AUC: 0.71~0.74) than previous TE prediction models for AF (AUC: 0.66~0.69), an...
Source: AMIA Annual Symposium Proceedings - August 8, 2017 Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research

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

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