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预测建模用于识别高利用卫生和社会服务的老年人

Heba Sourkatti¹, Juha Pajula¹, Teemu Keski-Kuha²

¹VTT Technical Research Centre of Finland Ltd, Espoo, Finland.

Scandinavian journal of primary health care

|July 3, 2024

概括

机器学习模型可以识别高风险的老年人过度使用健康和社会服务. 早期检测有助于预防性干预,尽管使用更多数据可以提高性能.

科学领域:

老年学是指老年学的学科.
医疗保健服务研究医疗服务研究
医疗保健中的机器学习

背景情况:

预测建模在临床环境中是成功的,但在老年人的多部门健康和社会服务方面却不太被探索.
老年人对服务的高利用率给医疗保健系统带来了挑战.
早期识别有风险的个体对于实施及时的预防策略至关重要.

研究的目的:

开发和评估机器学习模型,用于预测老年人过度使用健康和社会服务.
确定针对性预防干预的高风险群体.
评估早期检测服务过度利用的可行性.

主要方法:

在四年内利用了芬兰南部33374名老年人的匿名数据.
通过非计划的医疗保健访问和使用的独特服务总量来定义终点.
采用后勤回归和极端梯度提升 (XGBoost) 进行二进制分类,以70/30的火车/测试分割.

主要成果:

年龄和特定的健康问题 (例如,心理健康) 是高服务利用率的重要预测因素.
之前住院和城市居住与较低的服务利用风险有关.
模型的曲线下面积 (AUC) 为0.61的全队列和0.55-0.62的子组.

结论:

机器学习模型在预测老年人高服务利用率方面表现有前途.
由于多因素的影响,实现高预测准确度是具有挑战性的.
纳入社会经济数据可能会提高模型性能.

关键词:

机器学习是机器学习.卫生和社会服务使用情况年龄较大的成年人.预测建模预测建模数据的二次使用数据的二次使用.

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