文章摘要
叶子晨,汪嘉卉,陆取,薛鹏,江宇.临床预测模型开发的前期准备与框架构建[J].中华流行病学杂志,2025,46(8):1438-1445
临床预测模型开发的前期准备与框架构建
Preliminary preparation and framework construction for developing clinical prediction models
收稿日期:2025-01-14  出版日期:2025-08-21
DOI:10.3760/cma.j.cn112338-20250114-00038
中文关键词: 临床预测模型  模型开发  框架构建  数据处理
英文关键词: Clinical prediction model  Model development  Framework construction  Data processing
基金项目:中央高校基本科研业务费专项资金(3332024188)
作者单位E-mail
叶子晨 中国医学科学院北京协和医学院群医学及公共卫生学院, 北京 100730  
汪嘉卉 中国医学科学院北京协和医学院卫生健康管理政策学院, 北京 100730  
陆取 中国医学科学院北京协和医学院卫生健康管理政策学院, 北京 100730  
薛鹏 中国医学科学院北京协和医学院群医学及公共卫生学院, 北京 100730  
江宇 中国医学科学院北京协和医学院卫生健康管理政策学院, 北京 100730 jiangyu@pumc.edu.cn 
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中文摘要:
      临床预测模型通过利用临床数据和统计学方法,旨在提升医疗决策的准确性与效率,并改善患者的健康结局。这些模型在优化医疗决策和个性化治疗中具有重要作用。然而,当前许多研究在实际开发过程中普遍存在前期准备系统性不足的问题,具体表现为模型设计目标不明确、模型重复构建、变量选择缺乏临床相关性以及数据预处理不规范等情况,最终导致模型性能下降,临床适用性受限。为解决这些挑战,本研究对BMJ等相关文献进行了系统梳理,并结合实际研究经验,提出了系统化的前期准备流程,旨在为模型开发提供科学的指导框架,确保后续模型构建的高效性及其预测的准确性,从而为临床预测模型的应用与发展奠定基础。
英文摘要:
      Clinical prediction models, which utilize clinical data and statistical methods, aim to enhance the accuracy and efficiency of medical decision-making and improve patient health outcomes. These models play a crucial role in optimizing healthcare decisions and tailoring treatments to individual needs. However, many studies currently face systemic challenges during the development process, including unclear model design objectives, redundant model construction, lack of clinical relevance in variable selection, and irregular data preprocessing. These issues finally lead to reduced model performance and limited clinical applicability. To address these challenges, this study systematically reviews relevant literature, including articles from the BMJ, and draws on practical research experience to propose a structured preparation process. This process aims to provide a scientific guiding framework for model development, ensuring the efficiency of subsequent model construction and the accuracy of predictions, thus laying a foundation for the application and advancement of clinical prediction models.
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