Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon

The Biomedical Data Translator Consortium

Research output: Contribution to journalComment/debate

Abstract

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program (‘Translator’). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

Original languageEnglish (US)
Article number103325
JournalJournal of Biomedical Informatics
Volume100
DOIs
StatePublished - Dec 2019

Fingerprint

Program translators
Particulate Matter
Information Storage and Retrieval
Medical problems
Asthma
Obesity
Boidae
Information Dissemination
Electronic Health Records
Communication
Application programming interfaces (API)
Catchments
Health
Research

Keywords

  • Application programming interface
  • Clinical data
  • Hackathon
  • Multi-institutional collaboration
  • Open data
  • Team science

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Cite this

@article{109d938074a24353891f88ec573c996f,
title = "Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon",
abstract = "This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program (‘Translator’). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.",
keywords = "Application programming interface, Clinical data, Hackathon, Multi-institutional collaboration, Open data, Team science",
author = "{The Biomedical Data Translator Consortium} and Karamarie Fecho and Ahalt, {Stanley C.} and Saravanan Arunachalam and James Champion and Chute, {Christopher G.} and Sarah Davis and Kenneth Gersing and Gustavo Glusman and Jennifer Hadlock and Jewel Lee and Emily Pfaff and Max Robinson and Eric Sid and Casey Ta and Hao Xu and Richard Zhu and Qian Zhu and Peden, {David B.}",
year = "2019",
month = "12",
doi = "10.1016/j.jbi.2019.103325",
language = "English (US)",
volume = "100",
journal = "Journal of Biomedical Informatics",
issn = "1532-0464",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma

T2 - Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon

AU - The Biomedical Data Translator Consortium

AU - Fecho, Karamarie

AU - Ahalt, Stanley C.

AU - Arunachalam, Saravanan

AU - Champion, James

AU - Chute, Christopher G.

AU - Davis, Sarah

AU - Gersing, Kenneth

AU - Glusman, Gustavo

AU - Hadlock, Jennifer

AU - Lee, Jewel

AU - Pfaff, Emily

AU - Robinson, Max

AU - Sid, Eric

AU - Ta, Casey

AU - Xu, Hao

AU - Zhu, Richard

AU - Zhu, Qian

AU - Peden, David B.

PY - 2019/12

Y1 - 2019/12

N2 - This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program (‘Translator’). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

AB - This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program (‘Translator’). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

KW - Application programming interface

KW - Clinical data

KW - Hackathon

KW - Multi-institutional collaboration

KW - Open data

KW - Team science

UR - http://www.scopus.com/inward/record.url?scp=85074882444&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074882444&partnerID=8YFLogxK

U2 - 10.1016/j.jbi.2019.103325

DO - 10.1016/j.jbi.2019.103325

M3 - Comment/debate

C2 - 31676459

AN - SCOPUS:85074882444

VL - 100

JO - Journal of Biomedical Informatics

JF - Journal of Biomedical Informatics

SN - 1532-0464

M1 - 103325

ER -