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Prediction modeling—part 1: regression modeling

预测建模 疾病 结果(博弈论) 回归分析 重症监护医学 选型 医学 临床实习 统计模型 肾病科 计算机科学 内科学 机器学习 数学 家庭医学 数理经济学
作者
Eric Au,Anna Francis,Amélie Bernier-Jean,Armando Teixeira‐Pinto
出处
期刊:Kidney International [Elsevier]
卷期号:97 (5): 877-884 被引量:21
标识
DOI:10.1016/j.kint.2020.02.007
摘要

Risk prediction models are statistical models that estimate the probability of individuals having a certain disease or clinical outcome based on a range of characteristics, and they can be used in clinical practice to stratify disease severity and characterize the risk of disease or disease prognosis. With technological advancements and the proliferation of clinical and biological data, prediction models are increasingly being developed in many areas of nephrology practice. This article guides the reader through the process of creating a prediction model, including (i) defining the clinical question and type of model, (ii) data collection and data cleaning, (iii) model building and variable selection, (iv) model performance, (v) model validation, (vi) model presentation and reporting, and (vii) impact evaluation. An example of developing a prediction model to predict mortality after intensive care unit admission for patients with end-stage kidney disease is also provided to illustrate the model development process. Risk prediction models are statistical models that estimate the probability of individuals having a certain disease or clinical outcome based on a range of characteristics, and they can be used in clinical practice to stratify disease severity and characterize the risk of disease or disease prognosis. With technological advancements and the proliferation of clinical and biological data, prediction models are increasingly being developed in many areas of nephrology practice. This article guides the reader through the process of creating a prediction model, including (i) defining the clinical question and type of model, (ii) data collection and data cleaning, (iii) model building and variable selection, (iv) model performance, (v) model validation, (vi) model presentation and reporting, and (vii) impact evaluation. An example of developing a prediction model to predict mortality after intensive care unit admission for patients with end-stage kidney disease is also provided to illustrate the model development process.
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