A linked data approach to assessing medical data

Vast amounts of medical data are now routinely collected. This data is often subsequently used in medical research. However, the quality of the data can vary widely. Existing automated approaches to data quality assurance largely rely on threshold rules that can miss errors requiring complex domain knowledge to identify. In this paper we describe a framework to assess the reliability of medical data using linked data and semantic web technologies. This approach has been evaluated in the Neuro-Intensive Care Unit domain, successfully identifying potential errors in the recorded observations, and indicating that various ontologies proposed by the medical and sensor network communities can be used to represent medical observation data.