Forecasting the future clinical events of a patient through contrastive learning.

ABSTRACT:

Objective

Deep learning models for clinical event forecasting (CEF) based on a patient's medical history have improved significantly over the past decade. However, their transition into practice has been limited, particularly for diseases with very low prevalence. In this paper, we introduce CEF-CL, a novel method based on contrastive learning to forecast in the face of a limited number of positive training instances.

Materials and methods

CEF-CL consists of two primary components: (1) unsupervised contrastive learning for patient representation and (2) supervised transfer learning over the derived representation. We evaluate the new method along with state-of-the-art model architectures trained in a supervised manner with electronic health records data from Vanderbilt Unive

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC9382392 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature