应用机器学习方法来预测肥胖风险,使用美国卫生行政索赔数据库
Casey Choong1, Alan Brnabic2, Chanadda Chinthammit2
1Eli Lilly and Company, Indianapolis, Indiana, USA choong_kar-chan@lilly.com.
BMJ open diabetes research & care
|September 26, 2024
概括
行政索赔数据低于报告的肥胖症. 机器学习模型使用索赔数据准确预测肥胖状况,改进了传统的诊断代码,以获得更好的公共卫生洞察力.
科学领域:
- 医疗信息学 医疗信息学
- 在医疗保健中的数据科学.
- 公共卫生监督 公共卫生监督
背景情况:
- 身体质量指数 (BMI) 记录在美国行政索赔数据库中通常是不完整的.
- 验证与BMI相关的诊断代码和预测肥胖状况对于准确的健康负担评估至关重要.
研究的目的:
- 验证与BMI相关的诊断代码的灵敏度和正预测值 (PPV).
- 应用机器学习 (ML) 模型来使用美国索赔数据预测肥胖状况.
主要方法:
- 从2013年1月到2019年12月,使用MarketScan探索索赔-EMR数据对692,119个人的回顾性分析.
- 基于索赔的肥胖状况与基于EMR的BMI (黄金标准) 的比较,以评估代码的准确性.
- 训练后勤回归,惩罚后勤回归,极端梯度提升 (XGBoost) 和使用保险索赔特征的随机森林模型.
主要成果:
- 索赔数据显示PPV高 (85.4-89.2%),但对肥胖诊断代码的敏感性低 (16.8-44.8%).
- 在预测肥胖症方面,XGBoost表现出卓越的表现,在曲线下面积 (AUC) 达到79.4%的最高水平.
- 肥胖诊断和住院肥胖诊断的数量是关键预测因素;XGBoost在没有明确的肥胖代码的情况下实现了74.1%的AUC.
结论:
- 在行政索赔数据库中,肥胖患病率报告不足.
- 机器学习模型显示,即使没有明确的肥胖诊断代码,提高肥胖预测准确度也具有显著的前景.
- 改善肥胖预测可以帮助从业者和纳税人估计肥胖负担并确定治疗需求.
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