AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
AutoScore-Survival is an method extension to AutoScore, and a novel machine learning framework to automate the development of interpretable time-to-event scores. AutoScore-Survival 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. AutoScore-Survival could seamlessly generate risk scores based on survival data, which can be easily implemented and validated in clinical practice. Moreover, it enables users to build transparent and interpretable time-to-event scores quickly in a straightforward manner.
AutoScore-Survival has been merged with the AutoScore package. Please visit AutoScore bookdown page for a full tutorial.
Xie F, Ning Y, Yuan H, Goldstein BA, Ong MEH, Liu N, Chakraborty B. AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data Journal of biomedical informatics, 125 (2022) (https://doi.org/10.1016/j.jbi.2021.103959)
Xie F, Chakraborty B, Ong MEH, Goldstein BA, Liu N. AutoScore: A Machine Learning-Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records. JMIR Medical Informatics 2020;8(10):e21798 (http://dx.doi.org/10.2196/21798)
- Feng Xie (Email: xief@u.duke.nus.edu)
- Nan Liu (Email: liu.nan@duke-nus.edu.sg)
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)