Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.
High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medical supervision required for phenotyping algorithm (PA) estimation and evaluation. To address this challenge, numerous weakly-supervised learning methods have been proposed. However, there is a paucity of methods for reliably evaluating the predictive performance of PAs when a very small proportion of the data is labeled. To fill this gap [...]
Author(s): Gao, Jianhui, Bonzel, Clara-Lea, Hong, Chuan, Varghese, Paul, Zakir, Karim, Gronsbell, Jessica
DOI: 10.1093/jamia/ocad226