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预测艾滋病毒感染者的生活质量:一个集成多维决定因素的机器学习模型

Meilian Xie¹, Zhiyun Zhang², Yanping Yu³

¹Nursing Management Department, Beijing Ditan Hospital, Capital Medical University, Beijing, China. xiemeilian@mail.ccmu.edu.cn.

Health and quality of life outcomes

|July 4, 2025

概括

机器学习模型准确地预测艾滋病毒感染者 (PLWH) 的生活质量 (QoL),并将症状负担确定为关键因素. 定制干预措施对于在艾滋病毒治疗期间维持长期健康至关重要.

科学领域:

人工智能在医学中的应用
公共卫生公共卫生
艾滋病毒/艾滋病研究研究

背景情况:

生活质量 (QoL) 对艾滋病毒感染者 (PLWH) 越来越重要,因为生存率有所提高.
预测和理解QOL趋势对于优化PLWH的长期健康结果至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测PLWH的QoL趋势.
确定影响质量生活的关键决定因素,以告知个性化干预措施.

主要方法:

一项纵向观察性研究涉及676个PLWH.
收集社会人口统计,临床变量, QoL (WHOQOL-HIV BREF) 和症状经验 (SRSS) 数据.
开发和评估多种ML模型,包括高斯过程 (GP) 和随机森林 (RF).

主要成果:

高斯过程 (GP) 模型实现了最高的预测性能 (AUC 0.811-0.815).
症状负担被确定为QoL最关键的预测因素.
决策曲线分析表明,在特定的概率值下,GP和MLP模型的增强效益.

结论:

机器学习,特别是GP模型,有效地预测了PLWH中的QoL,突出了症状负担的重大影响.
在ART持续时间和QoL之间存在非线性关系,由于治疗疲劳和毒性,可能会出现下降.
动态的心理社会支持和量身定制的干预措施是必要的,以维持在长期艾滋病毒护理的QoL.

关键词:

艾滋病毒/艾滋病病毒/艾滋病病毒机器学习机器学习预测模型的预测模型.生活质量生活的质量.经历症状的症状体验.

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