Phenotype Analysis of Early Risk Factors from Electronic Medical Records Improves Image-Derived Diagnostic Classifiers for Optic Nerve Pathology
Shikha Chaganti, Kunal P. Nabar, Katrina M. Nelson, Louise A. Mawn, Bennett A. Landman. “Phenotype Analysis of Early Risk Factors from Electronic Medical Records Improves Image-Derived Diagnostic Classifiers for Optic Nerve Pathology” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2017. Oral presentation.
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Abstract
We examine CT imaging and EMR data of 392 subjects with disorders of the optic nerve. We developed an automated image-processing pipeline that identifies the orbital structures within the human eye, and calculates descriptive measurements. We customized the PheWAS study to derive diagnostic EMR phenotypes. We used random forest classifiers to evaluate the predictive power of image-derived markers, EMR phenotypes, and clinical visual assessments in identifying disease cohorts from a control group. The addition of EMR phenotypes to the imaging markers improves the classification accuracy against controls. This study illustrates the importance of diagnostic context for interpretation of image-derived markers.
