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鉴定和验证可解释的早期慢性病预测模型:一个多中心的回顾性研究

Jiayi He^1,2, Xin Wang³, Peiqi Zhu⁴

¹School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, China.

EClinicalMedicine

|June 26, 2025

概括

机器学习准确地预测了在没有糖尿病或高血压的成年人中早期慢性病 (CKD). 这种早期检测提高了风险评估,可能会延迟疾病的进展并改善患者的治疗结果.

科学领域:

腎臟病學 (nephrology) 是一種醫學.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

慢性病 (CKD) 影响全球10%的成年人.
由于无症状的进展,早期CKD诊断具有挑战性.
及时干预对于预防末期脏病 (ESRD) 至关重要.

研究的目的:

使用机器学习开发早期CKD预测模型.
在没有糖尿病,高血压或冠状动脉心脏病的个人中提高CKD风险评估的准确性.
通过促进早期检测和干预,改善患者的治疗结果.

主要方法:

追溯多中心研究,包括CKD患者和健康个体.
应用六种机器学习方法 (例如,XGBoost) 进行CKD预测.
临床特征的整合:血液常规,尿液分析和基本信息.
用AUC和F1分数评估模型性能;用SHAP评估特征重要性.

主要成果:

XGBoost 模型表现出卓越的性能.
"血液常规 + 尿液分析 + 基本信息"的组合实现了高预测准确性 (AUC = 0.9235,外部验证AUC = 0.8962).
为早期CKD风险预测模型的临床应用开发了一个网络工具.

结论:

一个可解释的机器学习模型有效地预测了早期的CKD.
低成本的临床数据组合可以产生高预测准确度.
开发的模型显示了在早期CKD识别和降低进展风险方面临床应用的潜力.

关键词:

慢性脏疾病慢性脏疾病早期预测预测的早期预测机器学习机器学习预测模型的预测模型.这就是 SHAP SHAP 的意思.

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