一个机器学习模型,利用电子健康记录数据预测2型糖尿病的治疗惯性
C C McDaniel1, W-H Lo-Ciganic2,3,4,5, J Huang2
1Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, 4306 Walker Building, Auburn, AL, 36849, USA. cnc0027@auburn.edu.
Journal of endocrinological investigation
|December 31, 2023
概括
治疗惯性,定义为在2型糖尿病中延迟治疗强化,影响40%的患者. 机器学习模型有效地预测了这种惯性,健康的区域级社会决定因素提高了预测准确度.
科学领域:
- 糖尿病研究研究 糖尿病研究
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 治疗惯性是管理2型糖尿病的一个重大挑战,导致低于最佳的血糖控制.
- 了解治疗惯性的流行率和预测因素对于改善患者的治疗结果至关重要.
研究的目的:
- 估计2型糖尿病患者治疗惯性患病率.
- 开发和验证用于预测治疗惯性的机器学习模型.
- 评估区域健康社会决定因素 (SDOH) 的预测附加值.
主要方法:
- 使用OneFlorida电子健康记录 (EHR) 对31,087名2型糖尿病成年人的回顾性队列研究.
- 治疗惯性定义为在6个月内没有加强治疗的HbA1C≥7%.
- 用渐变增强机 (GBM),随机森林,弹性网和LASSO来开发预测模型.
主要成果:
- 在68,445名患者记录中,治疗惯性的患病率为39.80%.
- GBM模型显示出优异的预测性能 (C-统计 = 0.84).
- 纳入区域级SDOH显著提高了预测模型的区分能力.
结论:
- 机器学习模型,特别是GBM,可以有效地利用EHR数据预测2型糖尿病的治疗惯性.
- 纳入SDOH提供了宝贵的额外预测能力,突出了它们在糖尿病护理中的重要性.
- 这些预测模型具有临床适用性,用于识别高风险患者并优化糖尿病管理.
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