A novel method for causal structure discovery from EHR data and its application to type-2 diabetes mellitus.

ABSTRACT: Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large E

SUBMITTER: Shen X 

PROVIDER: S-EPMC8546093 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature