AutoScore: An Interpretable Machine Learning-Based Automatic Clinical ScoreGenerator
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.
| Version: | 1.1.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | tableone,pROC,randomForest,ggplot2,knitr,Hmisc,car,dplyr,ordinal,survival,tidyr,plotly,magrittr,randomForestSRC,rlang,survAUC,survminer |
| Suggests: | rpart,rmarkdown |
| Published: | 2025-08-01 |
| DOI: | 10.32614/CRAN.package.AutoScore |
| Author: | 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] |
| Maintainer: | Feng Xie <xief at u.duke.nus.edu> |
| BugReports: | https://github.com/nliulab/AutoScore/issues |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/nliulab/AutoScore |
| NeedsCompilation: | no |
| Citation: | AutoScore citation info |
| Materials: | README,NEWS |
| CRAN checks: | AutoScore results |
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