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机器学习:预测因狼爆发而入院的患者住院时间

Radu Grovu¹, Yanran Huo², Andrew Nguyen³

¹Internal Medicine Department, Staten Island University Hospital, Staten Island, NY, USA.

|October 13, 2023

概括

机器学习准确地预测了系统性红斑狼 (SLE) 爆发的长时间住院时间. 关键预测因素包括中央线路需求,急性透析和急性功能衰竭,优化SLE患者的护理.

科学领域:

风湿病学和健康信息学
机器学习在医疗保健中的应用

背景情况:

严重的系统性红斑狼 (SLE) 爆发需要住院治疗,这意味着巨大的成本负担.
预测建模可以优化SLE住院治疗的管理.
机器学习 (ML) 为预测SLE患者长期住院时间 (LOS) 提供了一种新的方法.

研究的目的:

开发和评估ML模型,以预测因狼爆发而入院的患者的长期医院LOS.
确定导致SLE住院LOS延长的关键特征.

主要方法:

利用了因狼爆发而入院的5831名患者的数据 (国家住院患者样本数据库2016-2018).
构建并比较了四个ML模型:XGBoost,线性支向量机,K近邻和物流回归.
使用ROC-AUC,PR-AUC,F1得分和准确度评估模型性能;使用SHAP值进行特征重要性分析.

主要成果:

XGBoost模型表现出优异的性能,ROC-AUC为0.87,PR-AUC为0.61,F1得分为0.56,准确率为95%.
预测长期LOS的最有影响力的特征是需要中央线路,急性透析和急性功能衰竭.
脏和传染病并发症也是延长住院时间的重要预测因素.

结论:

机器学习模型在预测狼爆发住院LOS方面显示出显著的前景,其表现优于传统方法.
这些发现与现有文献一致,验证了ML在管理罕见类风湿性疾病事件中的实用性.
鉴定的特征可以为临床决策提供信息,以潜在地降低LOS和医疗保健成本.

关键词:

停留时间的长度.狼的爆发是狼的爆发.机器学习是机器学习.性狼是一种性狼.亚急性红血性狼是一种血红性狼.系统性红血性狼 (Systemic Lupus Erythematosus) 是一种全身性狼.

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