InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance.

ABSTRACT: Background: T ransformer-based language models have delivered clear improvements in a wide range of natural language processing (NLP) tasks. However, those models have a significant limitation; specifically, they cannot infer causality, a prerequisite for deployment in pharmacovigilance, and health care. Therefore, these transformer-based language models should be developed to infer causality to address the key question of the cause of a clinical outcome. Results: In this study, we propose an innovative causal inference model-InferBERT, by integrating the A Lite Bidirectional Encoder Representations from Transformers (ALBERT) and Judea Pearl's Do-calculus to establish potential causality in pharmacovigilance. Two FDA Adverse Event Reporting System case studies, including Anal

SUBMITTER: Wang X 

PROVIDER: S-EPMC8202286 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature