Interpretation of the Updated Guidance for Reporting Clinical Prediction Models that Use Regression or Machine Learning Methods
10.3760/cma.j.cn112338-20241105-00692
- VernacularTitle:基于回归或机器学习方法的临床预测模型报告更新指南解读
- Author:
Ben NIU
1
;
Mengjie WAN
;
Jue LIU
Author Information
1. 深圳大学管理学院，深圳 518060
- Publication Type:Journal Article
- Keywords:
Prediction model;
Artificial intelligence;
Prognosis;
Diagnosis;
Reporting guidelines
- From:
Chinese Journal of Epidemiology
2025;46(8):1451-1458
- CountryChina
- Language:Chinese
-
Abstract:
Recently, the number of artificial intelligence methods used to develop clinical risk prediction models has rapidly increased. To ensure the value of clinical prediction model research, researchers must report the research content transparently, completely, and accurately. Updated Guidance for Reporting Clinical Prediction Models that Use Regression or Machine Learning Methods (TRIPOD+AI) was released in 2024 and covers a checklist of 27 major items. It aims to promote the complete reporting of global clinical prediction model research and facilitate research evaluation, model evaluation, and model implementation. This article interprets and compares aspects such as the formulation process, checklist content, applicable scenarios, and advantages of TRIPOD+AI, as well as the original Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist. It also analyzes an example of predicting the depression of elderly patients using artificial intelligence methods, providing references for researchers to standardize the reporting of clinical prediction models.
