机器学习方法的比较,以预测退伍军人队伍中气道压力附着的积极性
Anna M May1,2, Jarrod E Dalton3
1Geriatric Research Education and Clinical Center and Sleep Medicine Section, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, United States.
概括
使用机器学习预测睡眠呼吸暂停的正气道压力 (PAP) 治疗坚持,其临床效用有限. 早期的数据,比如30天的PAP使用,显著提高了预测准确度,以获得更好的患者干预.
科学领域:
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 对睡眠呼吸暂停的积极气道压力 (PAP) 治疗的坚持是一个重大的挑战,特别是在老兵人群中.
- 低于最佳的坚持需要改进的方法来识别那些可能受益于替代疗法或额外支持的患者.
- 准确预测坚持是优化治疗策略的关键.
研究的目的:
- 评估各种机器学习算法的有效性,以预测90天的PAP治疗效果.
- 通过使用电子健康记录数据,确定遵守的关键预测因素.
- 评估个性化干预预测试模型的临床实用性.
主要方法:
- 分析了从2010年1月1日到2015年6月30日接受PAP治疗的5,047名患者的队列.
- 从电子健康记录中提取了人口统计,并发病率和药物治疗数据.
- 多个机器学习算法,包括渐变增强机器,被用来预测90天的坚持,使用根平均平方误差 (RMSE) 评估性能.
主要成果:
- PAP治疗的中位坚持率为36.7%,具有显著的可变性.
- 梯度增强机器模型实现了最佳性能 (RMSE 37.2),但没有早期坚持数据,它在临床上是没有用处的.
- 结合30天PAP数据和原始诊断/药物信息,RMSE提高到24.27,提高了预测能力.
结论:
- 目前使用电子医疗记录数据的机器学习模型表明,预测PAP治疗遵守的临床性能不足.
- 早期坚持数据 (30天) 和详细的患者信息对于提高预测准确性至关重要.
- 需要制定更有效的预测措施,以实现个性化干预,并提高患者对睡眠呼吸暂停治疗的坚持.
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