在糖尿病管理计划期间主动识别糖尿病患者有不受控制结果的风险:使用机器学习的概念化和发展研究
Arash Khalilnejad1, Ruo-Ting Sun1, Tejaswi Kompala1
1Teladoc Health, Purchase, NY, United States.
机器学习模型现在可以在远程监控程序中预测不受控制的糖尿病风险. 这使得个性化干预可以改善患者的治疗结果,并预防并发症.
科学领域:
- 糖尿病管理和远程医疗服务
- 机器学习在医疗保健中的应用.
- 慢性疾病的预测建模.
背景情况:
- 远程医疗的进步使得识别糖尿病管理的高风险个体成为可能.
- 预测建模对于改善糖尿病护理至关重要.
- 远程糖尿病监测计划 (RDMP) 提供有针对性的支持.
研究的目的:
- 开发一种新的机器学习 (ML) 方法,在RDMP中主动识别患有失控糖尿病风险的参与者.
- 预测远程监测计划参与者的12个月糖尿病风险.
主要方法:
- 使用了来自Livongo糖尿病RDMP的注册表数据.
- 开发了每月检查点 (月-0至月-11) 的动态预测ML模型.
- 纳入的参与者属性:调查数据,血糖 (BG) 水平,药物填充和健康信号. 模型使用光梯度增强机与超参数调进行训练.
主要成果:
- 机器学习模型在识别有风险的参与者方面表现出强的表现.
- 在可观察到的有风险的个体中,回忆率在70%-94%之间,精度在40%-88%之间.
- 模型的性能在程序过程中得到了改善,突出了参与数据的价值.
结论:
- 积极的ML模型准确地识别了患有失控糖尿病风险的参与者,准确度高,可概括.
- 个性化干预可以根据在不同计划阶段识别的风险实施.
- 这种方法推进了大规模的远程监测,预防并发症并改善血糖控制.
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