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基于机器学习的生物化学标记的识别,以预测在高代谢风险的患者肝硬化症的生物化学标记
Yuan Tian1,2, Hang-Yi Zhou1,2, Ming-Lin Liu1,2
1Department of Chinese Medicine, Changhai Hospital, Naval Medical University, Shanghai 200433, China.
World journal of gastroenterology
|July 31, 2025
概括
在高风险人群中,一种新的非侵入性工具有效地识别了代谢相关脂肪肝疾病 (MAFLD) 和肝脂肪症患者,改善了查并降低了医疗保健成本.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 代谢相关的脂肪肝疾病 (MAFLD) 是一种普遍存在的慢性肝病.
- 目前的查方法,如超声波,不足以在健康检查中识别有风险的个体.
- MAFLD往往是未被认可的,特别是在具有多种代谢风险因素的人群中.
研究的目的:
- 开发一种快速而准确的方法来识别具有高代谢风险的个体的肝硬化症.
- 为了降低与MAFLD诊断和管理相关的整体医疗成本.
- 引入一种用于早期检测MAFLD的非侵入性工具.
主要方法:
- 对前性队列研究数据集的分析,以确定MAFLD风险预测因素.
- 应用LASSO和递归特征消除来进行特征选择.
- 开发和验证四个机器学习模型,用于预测肝硬化症.
主要成果:
- 开发了一种具有10个特性的XGBoost模型,达到0.82.8的AUC.
- 该模型在711名参与者 (504名MAFLD与207名非MAFLD) 上得到验证.
- 最初的试验阶段显示了86.2%的代谢异常和70.8%的肝硬化.
结论:
- 已经开发出了一种有价值的非侵入性工具,用于在高代谢风险人群中识别肝硬化症.
- 这种工具有可能提高对MAFLD的查效率.
- 这种工具的实施可能会导致医疗费用大幅减少.
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