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.
| Version: |
0.3.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
tableone, pROC, randomForest, ggplot2, rpart, knitr |
| Suggests: |
rmarkdown |
| Published: |
2022-04-08 |
| Author: |
Feng Xie  [aut,
cre],
Yilin Ning [aut],
Han Yuan [aut],
Mingxuan Liu
[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 |
| CRAN checks: |
AutoScore results |
Documentation:
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