Package: AutoScore 1.0.0

AutoScore: An Interpretable Machine Learning-Based Automatic Clinical Score Generator

A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The details are described in our research paper<doi:10.2196/21798>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.

Authors:Feng Xie [aut, cre], Yilin Ning [aut], Han Yuan [aut], Mingxuan Liu [aut], Siqi Li [aut], Ehsan Saffari [aut], Bibhas Chakraborty [aut], Nan Liu [aut]