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机器学习模型的开发和经济评估,以预测2型糖尿病中的糖化血红蛋白
Yi-Tong Tong1, Guang-Jie Gao2, Huan Chang2
1Chengdu Second People's Hospital, Chengdu, Sichuan, China.
Frontiers in pharmacology
|July 17, 2023
概括
机器学习模型可以识别需要HbA1c测试的2型糖尿病 (T2D) 患者,优化管理. 这种方法可以改善个性化护理,并通过查血糖控制不良而降低医疗保健成本.
科学领域:
- 内分泌学和新陈代谢学
- 医疗保健中的人工智能
- 糖尿病管理 糖尿病管理
背景情况:
- 糖化血红蛋白 (HbA1c) 对于诊断和监测2型糖尿病 (T2D) 是至关重要的.
- 当前现实世界的监测频率往往不足以达到推的指导方针.
- 监测效率低下可能导致患者管理不足,医疗保健支出增加.
研究的目的:
- 开发和优化机器学习 (ML) 模型,用于选需要HbA1c测试的T2D患者.
- 为个性化T2D管理确定低血糖控制的关键预测因素.
- 与标准实践相比,评估基于ML的查的经济效益.
主要方法:
- 利用了980名T2D患者的回顾性数据,包括HbA1c和其他临床变量.
- 采用数据预处理,归算技术 (例如,修改随机森林),特征选 (ElasticNet) 和ML算法 (LightGBM).
- 基于预测性能和模型效益 (MB) 的192个模型进行了比较,并纳入了经济因素.
主要成果:
- 最好的ML模型,包括禁食血糖 (FBG) 和其他特征,实现了高预测精度 (AUC 0.972,AUPRC 0.944).
- 该模型显示了43475.750日元的显著模型收益 (MB),表明了经济优势.
- 关键预测因素包括FBG,先前的HbA1c,饮食,健康状况,甲福林类型,测量间隔,EQ-5D得分,职业和年龄.
结论:
- 机器学习模型,特别是基于LightGBM的方法,有效地识别了需要HbA1c测试的T2D患者.
- 模型效益 (MB) 作为一个有价值的指标来评估ML模型在临床和经济环境中的表现.
- 开发的模型支持个性化的T2D管理,潜在地改善患者的治疗结果和优化资源配置.
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