机器学习来识别前缓和缓:一个多中心,回顾性队列研究
Yue Chen1,2,3, Chenan Liu1,2,3, Xin Zheng1,2,3
1Department of Gastrointestinal Surgery/Clinical Nutrition, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, China.
概括
这项研究开发了机器学习模型,以使用患者特征来识别前缓和缓. 这些模型有助于临床医生早期检测和诊断前,改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 老年病的医生 老年病的医生
- 机器学习在医学中的应用
背景情况:
- 早期检测甲前对于管理甲前至关重要,但识别仍然具有挑战性.
- 在有效的预防和治疗策略中,检测前是至关重要的.
- 目前用于识别前症的方法缺乏简单性和效率.
研究的目的:
- 开发一种简单的方法来检测癌症前.
- 为了区分前的特征和的特征.
- 为了创建准确的预测模型,用于预卡谢和卡谢.
主要方法:
- 利用机器学习 (ML) 模型,对3896名参与者的基线特征进行训练.
- 采用变量重要性分析来完善ML模型以获得最佳性能.
- 使用接收器操作特征 (ROC) 值和校准曲线验证的模型准确性.
主要成果:
- 开发了两个逻辑回归模型来检测缓解症和前缓解症,其AUC值分别为0.830和0.701.
- 识别了缓冲症 (饮食变化,手臂周长,HDL,CAR) 和前缓冲症 (饮食变化,血清肌素,HDL,手握强度,CAR) 的关键指标.
- 模型表现出良好的准确性和校准性,促进了临床应用.
结论:
- 成功开发和验证了ML模型,用于识别前缓和缓.
- 这些模型为临床医生提供了一个实用的工具,以改善早期发现和诊断前.
- 这些发现支持将ML纳入营养评估的临床实践.
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