Autonomous Decentralized Privacy-Enabled Data Preparation Architecture for Multicenter Clinical Observational Research

Tailoring treatment and clinical decision making to a person's unique characteristics is the next milestone for healthcare informatics, but for it to be accomplished, big data analytics for identifying risk factors and other hidden patterns among patients become paramount. In future these analytics will take the form of multicenter observational research, for which data preparation is vital. Specifically, quality data must be obtained in a timely manner while protecting the privacy of patients in the health records shared among researchers. Furthermore, the coordination and cooperation of a fluctuating number of medical data sources containing these records for clinical data distribution is an additional requirement in multicenter studies. Thus, we propose an autonomous decentralized, privacy-enabled data preparation architecture and novel SEDTM algorithm to meet these requirements, censuring sensitive information via filtration, and extracting relevant clinical data with a fully automated approach. Our evaluation demonstrates a 40% - 60% increase in the retrieval of quality patient data, compared to traditional semantic similarity, for our proposed SEDTM algorithm.