{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T04:22:33Z","timestamp":1770697353594,"version":"3.49.0"},"reference-count":92,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T00:00:00Z","timestamp":1614729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1 TR003107"],"award-info":[{"award-number":["UL1 TR003107"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.<\/jats:p>","DOI":"10.3390\/informatics8010016","type":"journal-article","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T05:10:16Z","timestamp":1614748216000},"page":"16","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8978-1565","authenticated-orcid":false,"given":"Mahanazuddin","family":"Syed","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4761-5972","authenticated-orcid":false,"given":"Shorabuddin","family":"Syed","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1460-9867","authenticated-orcid":false,"given":"Kevin","family":"Sexton","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"},{"name":"Department of Surgery, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"},{"name":"Department of Health Policy and Management, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]},{"given":"Hafsa Bareen","family":"Syeda","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2652-5935","authenticated-orcid":false,"given":"Maryam","family":"Garza","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]},{"given":"Meredith","family":"Zozus","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA"}]},{"given":"Farhanuddin","family":"Syed","sequence":"additional","affiliation":[{"name":"Shadan Institute of Medical Sciences, College of Medicine, Hyderabad, Telangana 500086, India"}]},{"given":"Salma","family":"Begum","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]},{"given":"Abdullah Usama","family":"Syed","sequence":"additional","affiliation":[{"name":"Department of Information Science, University of Arkansas at Little Rock (UALR), Little Rock, AR 72205, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2698-8075","authenticated-orcid":false,"given":"Joseph","family":"Sanford","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"},{"name":"Department of Anesthesiology, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6314-5683","authenticated-orcid":false,"given":"Fred","family":"Prior","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR 72205, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: Past, present and future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. 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