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使用机器学习预测老年人住院的情况

Raymundo Buenrostro-Mariscal¹, Osval A Montesinos-López¹, Cesar Gonzalez-Gonzalez¹

¹School of Telematics, University of Colima, Colima 28040, Mexico.

Geriatrics (Basel, Switzerland)

|January 23, 2025

概括

预测老年人住院的情况至关重要. 一个随机森林模型确定了功能限制,年龄和脑血管事故作为关键预测因素,有助于医疗保健资源分配.

科学领域:

老年学是指老年学的学科.
公共卫生公共卫生
数据科学数据科学数据科学

背景情况:

墨西哥老年人住院率正在上升,原因是慢性疾病和紧张的医疗保健资源.
墨西哥健康与衰老研究 (MHAS) 提供了对理解住院趋势至关重要的纵向数据.

研究的目的:

使用随机森林 (RF) 算法,开发老年人住院预测模型.
通过分析变量重要性来确定住院的关键预测因素.

主要方法:

开发并使用各种数据分区策略和变量交互来评估RF模型.
修改后的嵌套交叉验证确保了模型的稳定性,使用灵敏度,特异性和kappa系数作为评估指标.
使用杂质和变重要性平均下降来评估变量重要性.

主要成果:

最佳模型 (ST2与相互作用和20%的测试比例) 实现了0.7215的灵敏度和0.4935.5的特异性.
关键预测因素包括功能限制 (31.1%),年龄 (12.75%),脑血管事故史 (12.4%) 和教育水平 (12.08%).
该模型有效地捕捉了健康和社会经济因素之间的复杂相互作用.

结论:

变量重要性分析提高了RF模型的解释性,用于预测老年人住院治疗.
结果为临床应用提供了见解,包括医院需求预测和资源优化.
未来的研究将探索对伴随性疾病的子组分析和先进的缺失数据技术.

关键词:

健康预测健康预测住院治疗住院治疗机器学习是机器学习.年龄较大的成年人.随机的森林随机的森林

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