开发和验证量子回归森林,用于预测手握和椅子站测试中的参考量子
Giulia Giordano1,2, Luca Mastrantoni3, Francesco Landi1,2
1Department of Geriatrics, Orthopedics and Rheumatological Sciences, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
Journal of cachexia, sarcopenia and muscle
|June 17, 2025
概括
机器学习模型准确地预测了手握强度和椅子站测试 (CST) 百分位数,用于早期发现肉类症. 这些工具可以帮助识别与年龄有关的功能衰退风险的个体.
科学领域:
- 老年学是一门学科.
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 肌肉力量对于诊断肉症至关重要.
- 萨科佩尼亚的诊断依赖于关键的功能指标,如手握强度和椅子站测试 (CST).
- 早期识别肉类风险对于及时干预至关重要.
研究的目的:
- 开发和训练一种机器学习模型,用于预测手握强度和CST的参考值和百分位数.
- 评估模型在大量社区成年人群中的表现.
- 促进在临床环境中早期检测肉症风险.
主要方法:
- 使用长寿检查 (Lookup) 8+项目的数据训练了一种量子回归森林 (QRF) 模型.
- 该模型使用了包括年龄,性别,身高,体重和BMI在内的变量.
- 使用R平方,平均平方误差,根平均平方误差和平均温克勒区间得分以90%的预测覆盖率来评估性能.
主要成果:
- 在测试组中,QRF模型实现了手握强度的0.65和CST的0.24的R平方.
- 预测覆盖率超过了手握强度的91%,CST的89%.
- 性别和年龄分别被确定为手握强度和CST的关键预测因素.
结论:
- 一个经过验证的QRF模型可以预测手握强度和CST的特定对象量子值.
- 这些模型提供了一种有希望的方法,以成本和时间高效地早期识别肉类风险.
- 该模型的预测输出可以作为衰老过程和功能衰退的生物标志物.
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