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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| Veröffentlicht in: | BioMed research international 2020, Vol.2020 (2020), p.1-8 |
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| creator | Patel, Rahul Murphy, Shawn N. Zagade, Akshay Wakle, Sachin B. Magdum, Pooja B. Desai, Somnath D. Ostrovsky, Yuri Pai Vernekar, Vishal V. Joshi, Shreekanth V. Wagholikar, Kavishwar B. Jain, Sheetal |
| 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. |
| doi_str_mv | 10.1155/2020/2851713 |
| format | Article |
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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.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2020/2851713</identifier><identifier>PMID: 32724799</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Application programming interface ; Biology ; Biology - methods ; Biomedical Research - methods ; Cloud Computing ; Codes ; Cohort Studies ; Data storage ; Data transfer (computers) ; Electronic Health Records ; Electronic medical records ; Electronic records ; Engineers ; Glucose ; Graphical user interface ; Humans ; Informatics ; Informatics - methods ; Information Storage and Retrieval - methods ; Knowledge ; Laboratories ; Logic ; Medical records ; Metadata ; Pipelines ; Queries ; Software ; Software engineering ; Structured Query Language-SQL ; Technology application ; User interface</subject><ispartof>BioMed research international, 2020, Vol.2020 (2020), p.1-8</ispartof><rights>Copyright © 2020 Kavishwar B. Wagholikar et al.</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>Copyright © 2020 Kavishwar B. Wagholikar et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2020 Kavishwar B. Wagholikar et al. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c456t-b3c617d4b546006f54b19425cb7b06a0f1f1ba44ccc69ade476514ef769fcfa13</cites><orcidid>0000-0001-5029-9694 ; 0000-0002-5281-2495 ; 0000-0002-7418-7248 ; 0000-0002-6219-861X ; 0000-0002-7367-6111 ; 0000-0002-4106-0347 ; 0000-0001-8900-9850</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366204/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366204/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4010,27902,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32724799$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Chao, Yun-Peng</contributor><contributor>Yun-Peng Chao</contributor><creatorcontrib>Patel, Rahul</creatorcontrib><creatorcontrib>Murphy, Shawn N.</creatorcontrib><creatorcontrib>Zagade, Akshay</creatorcontrib><creatorcontrib>Wakle, Sachin B.</creatorcontrib><creatorcontrib>Magdum, Pooja B.</creatorcontrib><creatorcontrib>Desai, Somnath D.</creatorcontrib><creatorcontrib>Ostrovsky, Yuri</creatorcontrib><creatorcontrib>Pai Vernekar, Vishal V.</creatorcontrib><creatorcontrib>Joshi, Shreekanth V.</creatorcontrib><creatorcontrib>Wagholikar, Kavishwar B.</creatorcontrib><creatorcontrib>Jain, Sheetal</creatorcontrib><title>Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><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. 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| source | MEDLINE; Wiley Open Access; PubMed Central; Alma/SFX Local Collection; PubMed Central Open Access |
| subjects | Application programming interface Biology Biology - methods Biomedical Research - methods Cloud Computing Codes Cohort Studies Data storage Data transfer (computers) Electronic Health Records Electronic medical records Electronic records Engineers Glucose Graphical user interface Humans Informatics Informatics - methods Information Storage and Retrieval - methods Knowledge Laboratories Logic Medical records Metadata Pipelines Queries Software Software engineering Structured Query Language-SQL Technology application User interface |
| title | Cloud Services for Patient Cohort Identification Using the Informatics for Integrating Biology and the Bedside Platform |
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