SAMGEP: A Semi-Supervised Method for Prediction of Phenotype Event Times

Overview

Leveraging large-scale electronic health record (EHR) data to estimate survival curves for clinical events can enable more powerful risk estimation and comparative effectiveness research. Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) yields a consistent and efficient survival curve estimator by leveraging a small size of current status labels and a large size of imperfect surrogate features.

Schematic of the SAMGEP algorthm.

Installation

Install stable version from CRAN:

Install development version from GitHub:

# install.packages("remotes")
remotes::install_github("celehs/SAMGEP")

Citation

Semi-supervised Calibration of Risk with Noisy Event Times (SCORNET) Using Electronic Health Record Data. Yuri Ahuja, Liang Liang, Selena Huang, Tianxi Cai. bioRxiv 2021.01.08.425976; doi: https://doi.org/10.1101/2021.01.08.425976