An ontology for very large numbers of longitudinal health records to facilitate data mining and machine learning

Abstract

Despite the extensive experience of the authors working in industry with a variety of electronic health records that worked well in their intended context, none currently available in reasonably large numbers seem to have ontologies and formats that will scale well to very large numbers of detailed cradle-to-grave longitudinal health records facilitating knowledge extraction. By that we mean data mining, Deep Learning neural nets and all related analytic and predictive methods for biomedical research and clinical decision support potentially applied to the health records of an entire nation. They are mostly far too complicated to support frequent high-dimensional analysis, which is required because such records will update (or should update) dynamically on a regular basis, will in future include new tests etc. acquired daily by translational medical research, and not least allow public health, research, and diagnostic, vaccine, and drug development teams to respond quickly to emergent epidemics like COVID-19. A Presidential Advisory team call in 2010 for interoperability and ease of data mining for medical records is discussed and the situation seems still not fully resolved. The solution appears to lie between efficient comma separated value files and the ability to embellish these with a moderate degree of more elaborate ontology. One recommendation is made here with discussion and analysis that should guide alternative and future approaches. It combines demographic, comorbidity, genomic, diagnostic, interventional, and outcomes information along with time/date stamping method appropriate to analysis, with facilities for special research studies. By using a “metadata operator”, a suitable balance between a comma separated values file and an ontological structure is possible.