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开发和多中心交叉测定验证一个可解释的预测模型的sarcopenic肥胖症:基于现有临床特征的机器学习方法
Rongna Lian1, Huiyu Tang1, Zecong Chen2
1Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Aging clinical and experimental research
|February 28, 2025
概括
我们开发了一种可解释的机器学习模型,使用临床数据预测形肥胖 (SO). 支持矢量机器模型准确地识别了SO风险因素,如BMI和性别,有助于在老龄化人口中早期检测.
科学领域:
- 老年学与公共卫生
- 人工智能在医学中的应用
- 代谢性疾病研究研究
背景情况:
- 肥胖症 (SO) 在老年人群中越来越令人担忧,与不良健康结果有关.
- 对SO的早期发现和干预对于改善老年人的健康至关重要.
- 现有的预测方法可能缺乏可访问性或可解释性.
研究的目的:
- 开发和验证一个可解释的预测模型,用于sarcopenic肥胖症 (SO).
- 为了利用易于获取的临床特征进行SO预测.
- 加强SO的早期检测和干预策略.
主要方法:
- 使用初步队列 (1,431名参与者) 进行模型开发和外部队列 (832名参与者) 进行验证.
- 采用了五种机器学习模型,包括支持矢量机 (SVM),以根据ESPEN和EASO标准预测SO.
- 应用了SHapley添加式解释 (SHAP) 来实现模型解释性,并开发了一个用于临床使用的网络应用程序.
主要成果:
- 一个8个特征模型,特别是SVM,显示出强大的预测性能 (AUC内部=0.862,外部=0.785).
- 确定的主要预测因素包括BMI,性别,子,腰和大腿周长,站立/坐着的时间和年龄.
- 通过SHAP分析,BMI和性别被确定为最有影响力的预测因素.
结论:
- 一个可解释的机器学习模型成功开发和验证了SO预测.
- 该模型为老年人SO风险评估提供了一种新,可访问和可解释的工具.
- 建议在不同人群中进一步验证,并将其纳入老年病评估中.
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