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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 The original AutoScore structure is described in the research paper<doi:10.2196/21798>. A full tutorial can be found here<https://nliulab.github.io/AutoScore/>. 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: |
1.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
tableone, pROC, randomForest, ggplot2, knitr, Hmisc, car, coxed, dplyr, ordinal, survival, tidyr, plotly, magrittr, randomForestSRC, rlang, survAUC, survminer |
| Suggests: |
rpart, rmarkdown |
| Published: |
2022-10-15 |
| DOI: |
10.32614/CRAN.package.AutoScore |
| Author: |
Feng Xie  [aut,
cre],
Yilin Ning [aut],
Han Yuan [aut],
Mingxuan Liu
[aut],
Seyed Ehsan Saffari
[aut],
Siqi Li [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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=AutoScore
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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