基于强化学习的 II 型糖尿病风险因素因果发现方法。
Causal discovery approach with reinforcement learning for risk factors of type II diabetes mellitus.
机构信息
College of Computer Science and Intelligent Education, Lingnan Normal University, Zhanjiang, 524048, Guangdong, China.
College of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian, 116028, Liaoning, China.
出版信息
BMC Bioinformatics. 2023 Jul 21;24(1):296. doi: 10.1186/s12859-023-05405-x.
BACKGROUND
Statistical correlation analysis is currently the most typically used approach for investigating the risk factors of type 2 diabetes mellitus (T2DM). However, this approach does not readily reveal the causal relationships between risk factors and rarely describes the causal relationships visually.
RESULTS
Considering the superiority of reinforcement learning in prediction, a causal discovery approach with reinforcement learning for T2DM risk factors is proposed herein. First, a reinforcement learning model is constructed for T2DM risk factors. Second, the process involved in the causal discovery method for T2DM risk factors is detailed. Finally, several experiments are designed based on diabetes datasets and used to verify the proposed approach.
CONCLUSIONS
The experimental results show that the proposed approach improves the accuracy of causality mining between T2DM risk factors and provides new evidence to researchers engaged in T2DM prevention and treatment research.
背景
目前，统计相关分析是研究 2 型糖尿病（T2DM）危险因素最常用的方法。然而，这种方法不能轻易揭示危险因素之间的因果关系，也很少能直观地描述因果关系。
结果
鉴于强化学习在预测方面的优势，本文提出了一种基于强化学习的 T2DM 危险因素因果发现方法。首先，构建了一个 T2DM 危险因素的强化学习模型。其次，详细介绍了 T2DM 危险因素因果发现方法的过程。最后，基于糖尿病数据集设计了几个实验，用于验证所提出的方法。
结论
实验结果表明，所提出的方法提高了 T2DM 危险因素之间因果关系挖掘的准确性，为从事 T2DM 预防和治疗研究的研究人员提供了新的证据。
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