Classification of imbalanced disparate medical data using ontology

Abstract: Through the digitization of healthcare, large volumes of data are generated and stored in healthcare operations. Today, a multitude of platforms and digital infrastructures are used for storage and management of data. The systems lack a common ontology which limits the interoperability between datasets. Limited interoperability impacts various areas of healthcare, for instance sharing of data between entities and the possibilities for aggregated machine learning research incorporating distributed data. This study examines how a random forest classifier performs on two datasets consisting of phase III clinical trial studies on small-cell lung cancer where the datasets do not share a common ontology. The performance is then compared to the same classifier’s performance on one dataset consisting of a connection of the two earlier mentioned sets where a common ontology is implemented. The study does not show unambiguous results indicating that a single ontology is creating a better performance for the random forest classifier. In addition, the conditions of entities within primary care in Sweden for undergoing a transition to a new platform for storage of data is discussed together with areas for future research.