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开发和验证机器学习算法来预测OneFlorida+患者中发生阿片类药物使用障碍的风险:预后建模研究

Jabed Al Faysal1,2, Weihsuan Lo-Ciganic3,4,5, Walid F Gellad3,4

  • 1Department of Pharmaceutical Outcomes & Policy, University of Florida, 1889 Museum Road, Malachowsky Hall, Suite 6300, Gainesville, FL, 32611, United States, 12566946603.

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此摘要是机器生成的。

机器学习模型可以预测开始阿片类药物治疗的患者的阿片类药物使用障碍 (OUD) 风险. 这种方法增强了对OUD的早期识别和干预,这是一个关键的公共卫生问题.

关键词:
一个佛罗里达州+通过外部验证.机器学习是机器学习."阿片类药物使用障碍"风险分层的风险分层.

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科学领域:

  • 临床信息学 临床信息学
  • 公共卫生 公共卫生
  • 医疗保健中的机器学习

背景情况:

  • 阿片类药物使用障碍 (OUD) 是美国的一大公共卫生危机.
  • 目前对OUD风险的查是有限的,缺乏个性化.
  • 机器学习 (ML) 提供了改善OUD风险预测的潜力.

研究的目的:

  • 开发和验证一种ML模型,以预测在开始阿片类药物治疗的成年人中发生3个月的OUD风险.
  • 使用电子健康记录 (EHR) 数据将患者分为临床可操作的风险组.

主要方法:

  • 利用OneFlorida+ EHR数据 (2017-2022) 进行模型开发和UPMC数据进行外部验证.
  • 包括182,083名没有之前OUD,癌症或过量服用阿片类药物处方的成年人.
  • 开发并比较了弹性网,LASSO,GBM和随机森林模型,使用了183个预测因素.

主要成果:

  • 梯度增强机 (GBM) 模型在验证中表现出强的性能 (C-统计=0.879),识别了年龄和疼痛史等关键预测因素.
  • 顶级风险十进制确定了~68%的OUD病例,预测值为3.26%.
  • 对UPMC数据的外部验证证实了GBM模型的区分能力 (C-统计=0.756).

结论:

  • 使用EHR数据开发的ML算法有效地预测和分层事件OUD风险.
  • 该模型显示了跨卫生系统应用的希望,以告知早期OUD干预.
  • 这种方法可以在临床环境中加强OUD预防策略.