Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform. (8th July 2020)

• Record Type: Journal Article
• Authors: Wagholikar, Kavishwar B.; Joshi, Shreekanth V.; Pai Vernekar, Vishal V.; Ostrovsky, Yuri; Desai, Somnath D.; Magdum, Pooja B.; Wakle, Sachin B.; Jain, Sheetal; Zagade, Akshay; Patel, Rahul; Murphy, Shawn N.
• Other Names: Chao Yun-Peng Academic Editor.
• Is Part Of: BioMed research international. Volume 2020(2020)
• Publication Date: 2020-07-08
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2020/2851713
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Despite the widespread use of the "Informatics for Integrating Biology and the Bedside" (i2b2) platform, there are substantial challenges for loading electronic health records (EHR) into i2b2 and for querying i2b2. We have previously presented a simplified framework for semantic abstraction of EHR records into i2b2. Building on our previous work, we have created a proof-of-concept implementation of cloud services on an i2b2 data store for cohort identification. Specifically, we have implemented a graphical user interface (GUI) that declares the key components for data import, transformation, and query of EHR data. The GUI integrates with Azure cloud services to create data pipelines for importing EHR data into i2b2, creation of derived facts, and querying for generating Sankey-like flow diagrams that characterize the patient cohorts. We have evaluated the implementation using the real-world MIMIC-III dataset. We discuss the key features of this implementation and direction for future work, which will advance the efforts of the research community for patient cohort identification.