Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform
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 r...
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doaj-89a5f4846f7f452cb64edd7632099d442020-11-25T03:45:54ZengHindawi LimitedBioMed Research International2314-61332314-61412020-01-01202010.1155/2020/28517132851713Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside PlatformKavishwar B. Wagholikar0Shreekanth V. Joshi1Vishal V. Pai Vernekar2Yuri Ostrovsky3Somnath D. Desai4Pooja B. Magdum5Sachin B. Wakle6Sheetal Jain7Akshay Zagade8Rahul Patel9Shawn N. Murphy10Harvard Medical School, Boston, MA, USAPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaPersistent Systems, Pune, IndiaHarvard Medical School, Boston, MA, USADespite 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.http://dx.doi.org/10.1155/2020/2851713 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kavishwar B. Wagholikar Shreekanth V. Joshi Vishal V. Pai Vernekar Yuri Ostrovsky Somnath D. Desai Pooja B. Magdum Sachin B. Wakle Sheetal Jain Akshay Zagade Rahul Patel Shawn N. Murphy |
spellingShingle |
Kavishwar B. Wagholikar Shreekanth V. Joshi Vishal V. Pai Vernekar Yuri Ostrovsky Somnath D. Desai Pooja B. Magdum Sachin B. Wakle Sheetal Jain Akshay Zagade Rahul Patel Shawn N. Murphy Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform BioMed Research International |
author_facet |
Kavishwar B. Wagholikar Shreekanth V. Joshi Vishal V. Pai Vernekar Yuri Ostrovsky Somnath D. Desai Pooja B. Magdum Sachin B. Wakle Sheetal Jain Akshay Zagade Rahul Patel Shawn N. Murphy |
author_sort |
Kavishwar B. Wagholikar |
title |
Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform |
title_short |
Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform |
title_full |
Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform |
title_fullStr |
Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform |
title_full_unstemmed |
Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform |
title_sort |
cloud services for patient cohort identification using the informatics for integrating biology and the bedside platform |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/2851713 |
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