开发和验证可解释的机器学习模型,用于预测老年住院患者的内在能力下降
WenYuan Li1, Ying Tang1, Tian Gao1
1School of Nursing, Evidence-Based Nursing Center, Lanzhou University, Lanzhou, 730011, China.
Experimental gerontology
|October 17, 2025
概括
机器学习有效地预测老年住院患者的内在能力下降. 支持矢量机模型确定了早期干预的关键风险因素,如心脏病和高血压.
科学领域:
- 老年学是一门学科.
- 人工智能的人工智能
- 临床医学 临床医学
背景情况:
- 内在能力下降是老年住院患者的一个重大问题.
- 早期识别有风险的个人对于及时干预和改善健康结果至关重要.
研究的目的:
- 开发和验证基于机器学习的风险预测模型,用于老年住院患者的内在能力下降.
- 确定与内在容量下降相关的关键预测因素.
主要方法:
- 利用了来自三个三级医院的533名老年住院患者的人口和疾病数据.
- 采用了四个机器学习算法,包括支持矢量机,来构建预测模型.
- 基于歧视,校准和临床实用性的评估模型,使用Shapley添加式扩展用于解释性.
主要成果:
- 一年内内在产能下降的发生率为50.8%.
- 支持矢量机模型表现出强大的预测性能,AUC为0.923 (训练),0.932 (测试) 和0.882 (验证).
- 确定的主要预测因素是心脏病,高血压,握力,年龄和甘油三.
结论:
- 支持矢量机模型有效地识别了老年住院患者,他们面临内在能力下降的高风险.
- 早期风险分层和有针对性的干预措施可以改善健康并促进该人口的健康衰老.
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