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A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes.
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.
Afiliação
  • Xie F; 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.
  • Ning Y; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Liu M; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Li S; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Saffari SE; 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.
  • Yuan H; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Volovici V; Department of Neurosurgery, Erasmus MC University Medical Center, 3015 GD Rotterdam, the Netherlands; Department of Public Health, Erasmus MC, 3015 GD Rotterdam, the Netherlands.
  • Ting DSW; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore; SingHealth AI Office, Singapore Health Services, Singapore 168582, Singapore.
  • Goldstein BA; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
  • Ong MEH; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore; Health Services Research Centre, Singapore Health Services, Singapore 169856, Singapore; Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore.
  • Vaughan R; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Chakraborty B; 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; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA; Department of Stati
  • Liu N; 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; SingHealth AI Office, Singapore Health Services, Singapore 168582, Singapore; Institute of Data Science, Na
STAR Protoc ; 4(2): 102302, 2023 May 12.
Article em En | MEDLINE | ID: mdl-37178115
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),1 Xie et al. (2022)2, Saffari et al. (2022)3 and the online tutorial https://nliulab.github.io/AutoScore/.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article