Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing.

ABSTRACT: Composite models that combine medical imaging with electronic medical records (EMR) improve predictive power when compared to traditional models that use imaging alone. The digitization of EMR provides potential access to a wealth of medical information, but presents new challenges in algorithm design and inference. Previous studies, such as Phenome Wide Association Study (PheWAS), have shown that EMR data can be used to investigate the relationship between genotypes and clinical conditions. Here, we introduce Phenome-Disease Association Study to extend the statistical capabilities of the PheWAS software through a custom Python package, which creates diagnostic EMR signatures to capture system-wide co-morbidities for a disease population within a given time interval. We investigate the eff

SUBMITTER: Chaganti S 

PROVIDER: S-EPMC6844192 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature