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一个用于开发可解释评分系统以预测常见类型临床结果的通用自动评分框架。

A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes.

作者信息

Xie Feng, Ning Yilin, Liu Mingxuan, Li Siqi, Saffari Seyed Ehsan, Yuan Han, Volovici Victor, Ting Daniel Shu Wei, Goldstein Benjamin Alan, Ong Marcus Eng Hock, Vaughan Roger, Chakraborty Bibhas, Liu Nan

机构信息

Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore.

Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.

出版信息

STAR Protoc. 2023 May 12;4(2):102302. doi: 10.1016/j.xpro.2023.102302.

Abstract

The AutoScore framework can automatically generate data-driven clinical scores in various clinical applications. Here, we present a protocol for developing clinical scoring systems for binary, survival, and ordinal outcomes using the open-source AutoScore package. We describe steps for package installation, detailed data processing and checking, and variable ranking. We then explain how to iterate through steps for variable selection, score generation, fine-tuning, and evaluation to generate understandable and explainable scoring systems using data-driven evidence and clinical knowledge. For complete details on the use and execution of this protocol, please refer to Xie et al. (2020), Xie et al. (2022), Saffari et al. (2022) and the online tutorial https://nliulab.github.io/AutoScore/.

摘要

AutoScore框架可以在各种临床应用中自动生成数据驱动的临床评分。在此,我们介绍一种使用开源AutoScore软件包为二元、生存和有序结局开发临床评分系统的方案。我们描述了软件包安装、详细的数据处理和检查以及变量排序的步骤。然后,我们解释如何迭代变量选择、评分生成、微调及评估步骤,以利用数据驱动的证据和临床知识生成易于理解和解释的评分系统。有关本方案使用和执行的完整详细信息,请参考Xie等人(2020年)、Xie等人(2022年)、Saffari等人(2022年)以及在线教程https://nliulab.github.io/AutoScore/

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd83/10200969/0957de7a4532/fx1.jpg

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