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Electronic Medical Records (EMRs) are increasingly being deployed at primary points of care and clinics for digital record keeping, increasing productivity and improving communication. In practice, however, there still exists an often incomplete picture of patient profiles, not only because of disconnected EMR systems but also due to incomplete EMR data entry - often caused by clinician time constraints and lack of data entry restrictions. To complete a patient's partial EMR data, we plausibly infer missing causal associations between medical EMR concepts, such as diagnoses and treatments, for situations that lack sufficient raw data to enable machine learning methods. We follow a knowledge-based approach, where we leverage open medical knowledge sources such as SNOMED-CT and ICD, combined with knowledge-based reasoning with explainable inferences, to infer clinical encounter information from incomplete medical records. To bootstrap this process, we apply a semantic Extract-Transform-Load process to convert an EMR database into an enriched domain-specific Knowledge Graph.

Using Knowledge Graphs to Plausibly Infer Missing Associations in EMR Data