| 牛奔,万梦洁,刘珏.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[J].中华流行病学杂志,2025,46(8):1451-1458 |
| 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 |
| Interpretation of the Updated Guidance for Reporting Clinical Prediction Models that Use Regression or Machine Learning Methods |
| 收稿日期:2024-11-05 出版日期:2025-08-21 |
| DOI：10.3760/cma.j.cn112338-20241105-00692 |
| 中文关键词: 预测模型 人工智能 预后 诊断 报告规范 |
| 英文关键词: Prediction model Artificial intelligence Prognosis Diagnosis Reporting guidelines |
| 基金项目:北京市自然科学基金重点项目（Z240004）；国家自然科学基金重点项目（72334004）；广东省普通高校重点领域专项（2022ZDZX2054）；广东省哲学社会科学规划一般项目（GD22CGL35） |
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| 中文摘要: |
| 近年来，用于开发临床预测模型的人工智能方法快速增加。为确保临床风险预测模型研究有价值，研究人员需透明、完整和准确地报告研究内容。回归或机器学习方法的临床预测模型报告更新指南（TRIPOD+AI）于2024年发布，涵盖一个包括27个主要项目的清单，旨在推动全球临床风险预测模型研究的完整报告，促进研究评估、模型评价和模型实施。本文从TRIPOD+AI的制定过程、清单内容、适用场景、优势以及与原有的针对个体的预后或诊断多因素预测模型报告规范（TRIPOD）清单比较等方面进行解读与讨论，并结合基于人工智能方法评估老年抑郁预测实例进行分析，以期为研究者规范报告临床风险预测模型提供参考。 |
| 英文摘要: |
| 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. |
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