DeepCausality: A general AI-powered causal inference framework for free text: A case study of LiverTox

ABSTRACT: Causality plays an essential role in multiple scientific disciplines, including the social, behavioral, and biological sciences and portions of statistics and artificial intelligence. Manual-based causality assessment from a large number of free text-based documents is very time-consuming, labor-intensive, and sometimes even impractical. Herein, we proposed a general causal inference framework named DeepCausality to empirically estimate the causal factors for suspected endpoints embedded in the free text. The proposed DeepCausality seamlessly incorporates AI-powered language models, named entity recognition and Judea Pearl's Do-calculus, into a general framework for causal inference to fulfill different domain-specific applications. We exemplified the utility of the proposed DeepCausality

SUBMITTER: Wang X 

PROVIDER: S-EPMC9763446 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature