Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

ABSTRACT: Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challenging and currently underserved field of feature engineering in machine learning workflows. Machine learning workflows are being increasingly used to analyze medical records with heterogeneous phenotypic, genotypic, and related medical terms to improve patient care. We performed a retrospective study using neuropathology reports from the German Neuropathology Reference Center for Epilepsy Surgery at Erlangen, Germany. This cohort included 312 patients who underwent epilepsy surgery and were labeled with one or more diagnoses, including dual pathol

SUBMITTER: Sahoo SS 

PROVIDER: S-EPMC9653502 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature