Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.

ABSTRACT:

Objective

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, we introduce a semi-supervised approach (ssROC) for estimation of the receiver operating characteristic (ROC) parameters of PAs (eg, sensitivity, specificity).

Materials and methods

ssROC uses a small labeled dataset to nonparametrically impute missing labels. The imputation

SUBMITTER: Gao J 

PROVIDER: S-EPMC10873838 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature