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一种评估预测模型的框架。

A framework for evaluating predictive models.

作者信息

Tan Yee-Leng, Saffari Seyed Ehsan, Tan Nigel Choon Kiat

机构信息

National Neuroscience Institute, Singapore; Duke-NUS Medical School, Singapore; Yong Loo Lin School of Medicine, Singapore.

National Neuroscience Institute, Singapore; Duke-NUS Medical School, Singapore; Yong Loo Lin School of Medicine, Singapore.

出版信息

J Clin Epidemiol. 2022 Oct;150:188-190. doi: 10.1016/j.jclinepi.2022.08.005. Epub 2022 Aug 13.

DOI:10.1016/j.jclinepi.2022.08.005
PMID:35973669
Abstract

Predictive models provide estimates on an individual's probability of having a disease or developing a disease/disease outcome. Clinicians often use them to support clinical decision-making. Many prediction models are published annually; online versions of models (such as MDCalc and QxMD) facilitate their use at the point of care. However, before using a model, the clinician should first establish that the model has undergone external validation demonstrating satisfactory predictive performance. Ideally, the model should also demonstrate improved outcomes from an impact analysis. This article summarizes the basic steps of predictive model evaluation, and is followed by an application example.

摘要

预测模型可对个体患某种疾病或发生某种疾病/疾病转归的概率进行估计。临床医生常使用这些模型来辅助临床决策。每年都会发布许多预测模型;模型的在线版本(如MDCalc和QxMD)便于在医疗现场使用。然而,在使用某个模型之前,临床医生应首先确定该模型已经过外部验证,证明其预测性能令人满意。理想情况下,该模型还应通过影响分析证明能带来更好的结果。本文总结了预测模型评估的基本步骤,并随后给出一个应用示例。

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