An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models.

ABSTRACT: OBJECTIVE:We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-domain and multidomain relation extraction from electronic health record (EHR) notes. MATERIALS AND METHODS:We built multiple deep learning models with increased complexity, namely a multilayer perceptron (MLP) model and a CapNet model for single-domain relation extraction and fully shared (FS), shared-private (SP), and adversarial training (ADV) modes for multidomain relation extraction. Our models were evaluated in 2 ways: first, we compared our models using our expert-annotated cancer (the MADE1.0 corpus) and cardio corpora; second, we compared our models with the systems in the MADE1.0 and i2b2 challenges. RESULTS:Multidomain models outperform single-domain models by 0.7%-1.4% in F1 (t test P?

SUBMITTER: Li F 

PROVIDER: S-EPMC6562161 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature