使用基于XGBoost/GRU-ODE-Bayes的机器学习算法,预测新诊断的2型糖尿病患者的心血管并发症
Joonyub Lee1, Yera Choi2, Taehoon Ko3
1Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Endocrinology and metabolism (Seoul, Korea)
|November 21, 2023
概括
机器学习模型准确地预测了韩国2型糖尿病患者 (T2DM) 的心血管风险. 格鲁-奥德-贝叶斯模型对个性化风险评估和治疗策略具有很高的准确性.
科学领域:
- 心脏病学 心脏病学
- 内分泌学 在内分泌学.
- 数据科学数据科学数据科学
背景情况:
- 心血管疾病 (CVD) 对2型糖尿病 (T2DM) 患者构成重大威胁,需要精确的风险分层.
- 个性化心血管风险评估对于T2DM患者的有效预防和管理至关重要.
研究的目的:
- 为韩国新诊断的T2DM患者开发和评估基于机器学习 (ML) 的心血管风险引擎.
- 将ML模型的性能与传统的基于回归的方法进行比较.
主要方法:
- 从首尔圣玛丽医院 (2009年7月 - 2019年4月) 对26166名新诊断的T2DM患者的回顾性分析.
- 定义了特定的观察和结果周期,以准确捕捉与糖尿病有关的心血管事件.
- 采用8: 2列车测试分割,重复100次,使用受体操作特征曲线 (AUROC) 下面的区域进行模型评估的10倍交叉验证.
主要成果:
- 基于ML的风险引擎与传统回归模型相比,表现优越.
- 该XGBoost模型实现了一个AUROC的0.781±0.014.
- 封闭循环单位 (GRU) -普通微分方程 (ODE) -贝叶斯模型实现了最高的AUROC0.812±0.016.
结论:
- GRU-ODE-Bayes模型为韩国T2DM患者的心血管风险分层提供了一个高度准确和适用的工具.
- 这种基于ML的引擎为个性化治疗策略提供了宝贵的见解.
- 这些发现支持使用先进的ML技术来管理T2DM心血管风险.
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