基于两个远程监控数据集构建的与健康相关的生活质量预测模型
Matea Tashkovska1,2, Stefan Krsteski1,2, Emilija Kizhevska1,2,3
1Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia.
PloS one
|December 4, 2024
概括
预测充血性心力衰竭 (CHF) 患者的与健康相关的生活质量 (HRQoL) 需要个性化的模型. 一般模型失败,但个体患者数据和自我报告的HRQoL标签显著改善了更好的护理预测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 心脏病学 心脏病学
背景情况:
- 充血性心力衰竭 (CHF) 是一种无法治愈的疾病,需要以患者的生活质量 (QoL) 为重点的管理.
- 远程监测生理和环境参数为监测患者健康提供了一条途径.
- 预测与健康相关的QoL (HRQoL) 可以帮助积极的患者管理.
研究的目的:
- 开发和评估CHF个体患者HRQoL的预测模型.
- 研究个性化预测模型与一般模型之间的有效性.
- 评估数据统一和个性化归算对HRQoL预测准确性的影响.
主要方法:
- 利用来自CHF患者的两个不同的远程监控数据集.
- 采用了各种各样的归算技术,分类模型和评估指标.
- 专注于统一数据集,以构建跨数据集的预测模型.
- 将个性化模型和归算策略与一般模型进行比较.
主要成果:
- 一般预测模型显示,未见病患者的表现不佳.
- 个性化模型和归算方法显著提高了HRQoL预测准确度.
- 有效的个性化至关重要,特别是考虑到远程监控数据集中缺失数据的高率.
结论:
- 个性化预测模型优于预测患者HRQoL的一般模型.
- 个性化归算策略对于处理远程监控中缺失的数据至关重要.
- 未来的应用需要收集自我报告的HRQoL标签,以获得有效的患者支持.
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