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Analyzing the eICU Collaborative Research Database

Published: 20 August 2017 Publication History

Abstract

Patients in hospital intensive care units (ICUs) are physiologically fragile and unstable, generally have life-threatening conditions, and require close monitoring and rapid therapeutic interventions. Staggering amounts of data are collected in the ICU daily: multi-channel waveforms sampled hundreds of times each second, vital sign time series updated each second or minute, alarms and alerts, lab results, imaging results, records of medication and fluid administration, staff notes and more. Reducing the barriers to data access has the potential to accelerate knowledge generation and ultimately improve patient care. In this interactive tutorial we introduce the eICU Collaborative Research Database: a large, publicly available database created by the MIT Laboratory for Computational Physiology in partnership with the Philips eICU Research Institute. The database contains routinely collected data from over 200,000 admissions to intensive care units across the United States, with representation from 10-12% of US ICU beds. The data facilitates a breadth of research studies, such as investigations into treatment efficacy, discovery of clinical markers in illnesses, and the development of decision support models. Participants in the tutorial gain an overview of the eICU Collaborative Research Database, in particular being introduced to its structure, content, and limitations. Following this overview, participants explore a demo version of the database using a laptop in a hands-on project. This exercise requires minimal technical expertise and gives an insight into the type of study that can be carried out using the database. We also highlight the growing online community centered around secondary analysis of this data. All tutorial materials are open source and publicly available. The eICU Collaborative Research Database offers an unparalleled insight into ICU care. Access to the database is granted to legitimate researchers who request it, following completion of a training course in human subjects research and acceptance of a data use agreement. We anticipate that the research community will use this unique resource to further human knowledge in the field of critical care.

Cited By

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  • (2023)Database-based machine learning in sepsis deserves attentionIntensive Care Medicine10.1007/s00134-022-06961-149:2(262-263)Online publication date: 2-Jan-2023
  • (2021)A Survey on Knowledge Enhanced EHR Data Mining5th International Conference on Crowd Science and Engineering10.1145/3503181.3503202(124-131)Online publication date: 16-Oct-2021
  • (2020)Use of Do-Not-Resuscitate Orders for Critically Ill Patients with ESKDJournal of the American Society of Nephrology10.1681/ASN.202001008831:10(2393-2399)Online publication date: 27-Aug-2020
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    cover image ACM Conferences
    ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics
    August 2017
    800 pages
    ISBN:9781450347228
    DOI:10.1145/3107411
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 August 2017

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    Author Tags

    1. critical care
    2. data mining
    3. electronic health record

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    • Tutorial

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    • NIBIB

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    BCB '17
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    Acceptance Rates

    ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;
    Overall Acceptance Rate 254 of 885 submissions, 29%

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    Cited By

    View all
    • (2023)Database-based machine learning in sepsis deserves attentionIntensive Care Medicine10.1007/s00134-022-06961-149:2(262-263)Online publication date: 2-Jan-2023
    • (2021)A Survey on Knowledge Enhanced EHR Data Mining5th International Conference on Crowd Science and Engineering10.1145/3503181.3503202(124-131)Online publication date: 16-Oct-2021
    • (2020)Use of Do-Not-Resuscitate Orders for Critically Ill Patients with ESKDJournal of the American Society of Nephrology10.1681/ASN.202001008831:10(2393-2399)Online publication date: 27-Aug-2020
    • (2020)Temporal Trends in Critical Care Outcomes in U.S. Minority-Serving HospitalsAmerican Journal of Respiratory and Critical Care Medicine10.1164/rccm.201903-0623OC201:6(681-687)Online publication date: 15-Mar-2020
    • (2020)Brief introduction of medical database and data mining technology in big data eraJournal of Evidence-Based Medicine10.1111/jebm.1237313:1(57-69)Online publication date: 22-Feb-2020
    • (2019)Deep Learning Enabled Predicting Modeling of Mortality of Diabetes Mellitus PatientsPractice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)10.1145/3332186.3333262(1-6)Online publication date: 28-Jul-2019
    • (2018)The eICU Collaborative Research Database, a freely available multi-center database for critical care researchScientific Data10.1038/sdata.2018.1785:1Online publication date: 11-Sep-2018

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