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. 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.


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0.2.0 by Feng Xie, 4 months ago

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

Documentation:   PDF Manual  

GPL (>= 2) license

Imports tableone, pROC, randomForest, ggplot2, knitr

Suggests rmarkdown

See at CRAN