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

AutoScore Introduction

AutoScore is a novel machine learning framework to automate the development of interpretable clinical scoring models. AutoScore consists of six modules: 1) variable ranking with machine learning, 2) variable transformation, 3) score derivation, 4) model selection, 5) domain knowledge-based score fine-tuning, and 6) performance evaluation. The original AutoScore structure is elaborated in this article and its flowchart is shown in the following figure. AutoScore was originally designed for binary outcomes and later extended to survival outcomes and ordinal outcomes. AutoScore could seamlessly generate risk scores using a parsimonious set of variables for different types of clinical outcomes, which can be easily implemented and validated in clinical practice. Moreover, it enables users to build transparent and interpretable clinical scores quickly in a straightforward manner.

Please go to our bookdown page for a full tutorial on AutoScore usage.

Usage

The five pipeline functions constitute the 5-step AutoScore-based process for generating point-based clinical scores for binary, survival and ordinal outcomes.

This 5-step process gives users the flexibility of customization (e.g., determining the final list of variables according to the parsimony plot, and fine-tuning the cutoffs in variable transformation):

We also include several optional functions in the package, which could help with data analysis and result reporting.

Please go to our bookdown page for a full tutorial on AutoScore usage.

Citation

Core paper

Method extensions

Contact

AutoScore package installation

Install from GitHub or CRAN:

# From Github
install.packages("devtools")
library(devtools)
install_github(repo = "nliulab/AutoScore", build_vignettes = TRUE)

# From CRAN (recommended)
install.packages("AutoScore")

Load AutoScore package:

library(AutoScore)

Please go to our bookdown page for a full tutorial on AutoScore usage.