监督机器学习算法来预测艾滋病毒感染者的长期住院时间和风险:一项回顾性研究
Jialu Li1, Yiwei Hao2, Ying Liu1
1Clinical and Research Center of AIDS, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Frontiers in public health
|January 22, 2024
概括
监督机器学习模型有效预测艾滋病毒感染者住院时间和长时间住院风险 (PLWH). 这些预测工具可以帮助临床决策,并优化医疗保健资源分配.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 公共卫生 公共卫生
背景情况:
- 预测艾滋病毒感染者的住院时间和长时间住院风险对于及时的临床干预和高效的资源管理至关重要.
- 监督机器学习为在医疗保健环境中开发准确的预测模型提供了潜力.
研究的目的:
- 开发和验证监督机器学习模型,用于预测住院时间和PLWH中长时间住院的风险.
- 为这些预测任务确定最有效的机器学习算法.
主要方法:
- 使用回归模型 (RF,KNN,SVM,XGB) 预测住院时间,通过RMSE,MAE,MAPE和R2进行评估.
- 分类模型 (RF,KNN,SVM,NN,XGB) 预测了长时间住院的风险,通过准确性,PPV,NPV,特异性,敏感性和kappa进行评估.
- 内部验证包括AUROC,AUPRC,校准曲线和决策曲线等可视化指标.
主要成果:
- XGB模型在预测住院时间方面表现优异 (RMSE=16.81,R2=0.47).
- 该NN模型在预测长时间住院风险方面表现出色,显示高准确度 (0.7623) 和灵敏度 (0.8754).
- 可视化评估证实了模型的稳定性,NN获得了最高的AUROC (0.9779) 和AUPRC (0.773).
结论:
- 在PLWH中,XGB和NN模型是预测住院时间和长时间住院风险的有效工具.
- 基于这些模型的智能医疗预测系统的开发可以增强临床决策和减少医疗保健资源浪费.
- 机器学习应用程序在优化患者管理和艾滋病毒护理资源配置方面表现有前途.
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