A universal AutoScore framework to develop interpretable scoring systems for predicting common types of clinical outcomes
Feng Xie, Yilin Ning, Mingxuan Liu, Siqi Li, Seyed Ehsan Saffari, Han Yuan, Victor Volovici, Daniel Shu Wei Ting, Benjamin A. Goldstein, Marcus Eng Hock Ong, R. S. Vaughan, Bibhas Chakraborty, Nan Liu
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),1 Xie et al. (2022)2, Saffari et al. (2022)3 and the online tutorial https://nliulab.github.io/AutoScore/.