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开发一款机器学习模型原型,用于预测艾滋病毒感染者的生活质量指标

Gabriel Mercadal-Orfila^1,2, Joaquin Serrano López de Las Hazas³, Melchor Riera-Jaume⁴

¹Pharmacy Department, Hospital Mateu Orfila, Maón, Spain.

Integrated pharmacy research & practice

|January 28, 2025

概括

机器学习使用患者报告的数据准确地预测艾滋病毒感染者的健康结果. 这有助于推进基于证据的医学和个性化护理策略.

科学领域:

机器学习在医疗保健中的应用.
基于证据的医学取得了进步.
患者为中心的护理模式

背景情况:

基于证据的医学整合了科学证据,临床专业知识和患者价值观,以获得最佳的决策.
患者报告结果测量 (PROM) 和患者报告体验测量 (PREM) 对于评估治疗影响至关重要,特别是在艾滋病毒等慢性疾病中.
机器学习 (ML) 的整合意味着向更客观,数据驱动的医疗保健迈进.

研究的目的:

利用ML技术,根据PROM/PREM数据预测健康结果.
专注于艾滋病毒感染者,以加强他们的健康管理.

主要方法:

分析了来自1200多名艾滋病毒感染者的PROM/PREM数据.
通过NAVETA远程医疗系统应用ML随机森林回归.
开发和评估用于预测准确性的ALGOPROMIA ML模型.

主要成果:

阿尔戈普罗米亚ML模型对几个仪器 (例如,MOS30 VIH Adj. R2 = 0.984) 的情况.
在不同的PROM/PREM仪器中,模型性能各不相同,其中一些显示中度准确性 (例如,TSQM Adj. R2 = 0.698) 的情况.
该模型保持了强大的可靠性,大多数仪器的预测误差低于0.2,对于WHOQoL HIV Bref (96.43%) 等关键措施,达到这一值的概率很高.

结论:

在艾滋病毒/艾滋病护理中,ML算法对预测PROM和PREM显著有前途.
整合ML增强了临床决策,并支持个性化,基于价值的治疗策略.
像NAVETA这样的远程医疗平台提供了一个可扩展的解决方案,用于在以患者为中心的护理中部署ML模型.

关键词:

在海上游泳,海上游泳这就是PROMs-PREMs.慢性艾滋病病毒管理基于证据的医学是基于证据的医学.医疗保健中的机器学习

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