The primary value that Graph data model brings to Healthcare services is to aggregate information from various heterogeneous data sources (e.g., Electronic Medical Records from Medical Data Centers or publicly-available Disease-Symptoms relations) whereas Machine Learning provides the ability to process such huge volume of medical data and learn meaningful underlying patterns or latent relationships. In this talk, we will explain how Oracle Labs and Oracle HS-GBU employ a combination of graph and machine learning techniques to develop efficient Healthcare Services. These healthcare services enable multiple crucial medical objectives like (a) matching Rare Patients with Clinical Trials (considerably important for Cancer treatments), as well as (b) searching for Similar Patients from a Medical Database based on previous medical records of the patients.
