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一个基于机器学习的预测模型,用于韩国2型糖尿病患者的糖尿病病

Kyung Ae Lee¹, Jong Seung Kim², Yu Ji Kim¹

¹Division of Endocrinology and Metabolism, Department of Internal Medicine, Jeonbuk National University Medical School, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju 54907, Republic of Korea.

Journal of clinical medicine

|March 27, 2025

概括

机器学习模型可以预测韩国2型糖尿病患者的糖尿病病 (DKD). XGBoost和后勤回归显示出高准确度,有助于早期评估DKD的风险.

科学领域:

腎臟病學 (nephrology) 是一種醫學.
内分泌学在内分泌学.
数据科学数据科学数据科学

背景情况:

糖尿病病 (DKD) 是2型糖尿病 (T2DM) 的重要并发症,导致末期病.
韩国T2DM患者DKD发病的预测模型有限,需要进一步研究.
早期识别DKD风险对于及时干预和管理至关重要.

研究的目的:

开发和验证基于机器学习 (ML) 的模型,用于预测韩国T2DM患者的DKD.
确定与DKD发展相关的关键临床和实验室因素.
评估开发的ML模型的通用性和临床实用性.

主要方法:

从六家韩国医院的电子健康记录进行了回顾性分析.
开发和内部验证使用Jeonbuk国家大学医院队列 (8: 2火车:测试分割).
外部验证使用来自另外五家医院的数据.
多个ML算法的比较,包括XGBoost,随机森林和后勤回归.
包括人口统计数据,并发症,药物和实验室结果.

主要成果:

在5120名T2DM患者中,1361人 (26.6%) 患有DKD.
在发育队列中,XGBoost表现出最高的预测性能 (AUC:0.8099).
外部验证证实了XGBoost (AUC:0.8113) 和后勤回归模型 (AUC:0.8228-0.8271) 的强大性能.
关键预测因素包括年龄,基线估计的膜过率,肌素,血红蛋白和HbA1c水平.

结论:

基于ML的方法,特别是XGBoost和后勤回归,显示了在韩国T2DM患者中对DKD预测的巨大潜力.
经过验证的模型为早期DKD风险评估提供了有价值的工具.
该模型在多个机构的通用性支持其临床适用性.

关键词:

人工智能的人工智能是人工智能.临床应用临床应用糖尿病病糖尿病病机器学习是机器学习.预测模型预测模型风险预测风险预测 2型糖尿病是什么?

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