在成人和住院的老年人中识别与药物相关的跌倒风险:一种机器学习方法
Amanda Pestana da Silva1, Henrique Dias Pereira Dos Santos2, Janete de Souza Urbanetto1
1School of Medicine, Graduate Program in Biomedical Gerontology (GERONBIO), Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
Geriatric nursing (New York, N.Y.)
|August 15, 2024
概括
机器学习模型可以根据处方药物预测住院成人和老年人的跌倒风险. 针对老年人的特定模型显示,与药物相关的跌倒风险的预测准确性更好.
科学领域:
- 老年学是一门学科.
- 医疗信息学 医疗信息学
- 药理学 药理学是指药理学的学科.
背景情况:
- 跌倒是住院成年人和老年人面临的重大风险.
- 处方药是一种已知的导致跌倒风险的因素.
- 需要准确的预测模型来识别高风险个体.
研究的目的:
- 开发和验证机器学习模型,用于预测住院成人和老年人的跌倒风险.
- 为了确定处方药对跌倒风险的影响.
- 为了比较成年人和老年人子组之间的模型性能.
主要方法:
- 一项涉及9037名住院成人和老年人的病例控制研究.
- 使用后勤回归,天真海湾,随机森林和梯度增强算法的分析.
- 为成年人和老年人子组开发特定模型.
主要成果:
- 最好的整体模型在成年子组中达到0.776的曲线下面积 (AUC),在老年人子组中达到0.628.
- 专门针对老年人的梯度增强模型显示了性能改善,AUC为0.71.
- 药物特定的跌倒风险模型在老年人中表现更好,而不是一般人群模型.
结论:
- 机器学习模型可以有效地预测住院人群中与处方药物相关的跌倒风险.
- 针对老年人量身定制的模型显示了与药物相关的跌倒的卓越预测准确性.
- 这些发现支持开发有针对性的干预措施,以减轻老年人中跌倒风险.
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