EXIST:检查过度脂肪的风险Ty-Machine学习预测与肥胖相关的并发症
Alexander Turchin1,2, Fritha J Morrison1, Maria Shubina1
1Brigham and Women's Hospital Boston Massachusetts USA.
Obesity science & practice
|January 24, 2024
概括
使用电子健康记录开发了与肥胖相关并发症的预测模型. 这些模型可以识别高风险患者,帮助临床决策和人口健康管理.
科学领域:
- 医疗信息学医学信息学
- 临床流行病学临床流行病学
- 生物统计学 生物统计学
背景情况:
- 肥胖增加了许多健康问题的风险,包括心脏病和癌症.
- 目前,专门针对超重或肥胖个体存在有限的预测模型.
- 开发有针对性的预测工具对于管理与肥胖相关的健康风险至关重要.
研究的目的:
- 创建和验证肥胖相关并发症的预测模型.
- 为了识别超重和肥胖的个体,对不良健康结果的高风险.
主要方法:
- 在2000-2019年期间利用了成年人 (BMI 25-80 kg/m2) 的电子健康记录数据.
- 开发了使用Lasso-Cox和随机生存森林 (RSF) 的预测模型,用于九个长期结果.
- 在一个单独的测试数据集上评估模型超过100次复制;开发了节的模型 (<10个变量).
主要成果:
- 在433272名患者的中位数5.6年的随访期间,结果发生率有所不同 (1.8%的膝关节置换为11.7%的动脉样硬化心血管疾病).
- RSF模型实现了哈雷尔C指数从0.702 (肝脏) 到0.896 (死亡).
- 拉索-科克斯模型显示了类似的性能 (0.694-0.891),省钱的模型范围从0.675 (肝脏) 到0.850 (膝盖置换).
结论:
- 预测建模有效地识别出患有肥胖并发症高风险的患者.
- 可解释的考克斯模型提供了与机器学习方法可比的性能.
- 这些模型可以为人口健康管理和肥胖的临床治疗决策提供信息.
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