开发和验证可解释的基于机器学习的计算器,用于预测减肥手术后的5年体重轨迹:一项跨国回顾性队列研究SOPHIA
Patrick Saux1, Pierre Bauvin2, Violeta Raverdy2
1Université de Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStAL, France.
The Lancet. Digital health
|August 31, 2023
概括
这项研究开发了一种国际验证的机器学习模型,用于预测减肥手术后个人的5年体重减轻. 该模型使用七个关键变量来预测减肥轨迹,帮助做出手术前临床决策.
科学领域:
- 腹部外科手术的结果
- 医疗保健中的机器学习
- 预测建模预测建模
背景情况:
- 减肥手术后的体重减轻在个体之间有很大差异.
- 在手术前预测患者的结果仍然是一个临床挑战.
研究的目的:
- 开发和验证一种机器学习模型,用于预测肥胖手术后个人的5年减肥轨迹.
- 为临床医生提供一个工具,帮助他们明智地做出手术前决策.
主要方法:
- 一项跨国追溯观察性研究,涉及来自欧洲,美洲和亚洲的12个队伍的10,231名成年患者.
- 使用最小绝对收缩和选择运算符 (LASSO) 和分类和回归树 (CART) 算法开发一个预测模型.
- 在5年随访时使用BMI的中位数绝对偏差 (MAD) 和根平均平方误差 (RMSE) 评估模型性能.
主要成果:
- 为模型选择了七个基线变量 (身高,体重,干预类型,年龄,糖尿病状态,糖尿病持续时间,吸烟状态).
- 该模型表现出良好的预测性能,平均绝对偏差为2.8 kg/m2和根平均平方误差为4.7 kg/m2的BMI在5年.
- 创建了一个用户友好的,基于Web的预测工具,将验证的模型纳入其中.
结论:
- 一个国际验证的基于机器学习的模型可以准确地预测常见的减肥干预后个人的5年减肥轨迹.
- 开发的预测工具可以帮助临床医生在患者管理和手术前咨询.
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