{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:08:20Z","timestamp":1761808100921,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,2,17]],"date-time":"2017-02-17T00:00:00Z","timestamp":1487289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Intensive care is a critical area of medicine having a multidisciplinary nature requiring all types of healthcare professionals. Given the critical environment of intensive care units (ICUs), the need to use information technologies, like decision support systems, to improve healthcare services and ICU management is evident. It is proven that unplanned and prolonged admission to the ICU is not only prejudicial to a patient's health, but also such a situation implies a readjustment of ICU resources, including beds, doctors, nurses, financial resources, among others. By discovering the common characteristics of the admitted patients, it is possible to improve these outcomes. In this study clustering techniques were applied to data collected from admitted patients in an intensive care unit. The best results presented a silhouette of 1, with a distance to centroids of 6.2 \u00d7 10\u221217 and a Davies\u2013Bouldin index of \u22120.652.<\/jats:p>","DOI":"10.3390\/info8010023","type":"journal-article","created":{"date-parts":[[2017,2,17]],"date-time":"2017-02-17T12:10:34Z","timestamp":1487333434000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Patients\u2019 Admissions in Intensive Care Units: A Clustering Overview"],"prefix":"10.3390","volume":"8","author":[{"given":"Ana","family":"Ribeiro","sequence":"first","affiliation":[{"name":"Centro ALGORITMI, University of Minho, Campus Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Filipe","family":"Portela","sequence":"additional","affiliation":[{"name":"Centro ALGORITMI, University of Minho, Campus Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Manuel","family":"Santos","sequence":"additional","affiliation":[{"name":"Centro ALGORITMI, University of Minho, Campus Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Ant\u00f3nio","family":"Abelha","sequence":"additional","affiliation":[{"name":"Centro ALGORITMI, University of Minho, Campus Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-6169","authenticated-orcid":false,"given":"Jos\u00e9","family":"Machado","sequence":"additional","affiliation":[{"name":"Centro ALGORITMI, University of Minho, Campus Azur\u00e9m, 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Fernando","family":"Rua","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, Centro Hospitalar do Porto, Largo do Prof. Abel Salazar, 4099-001 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,17]]},"reference":[{"key":"ref_1","first-page":"126","article-title":"Prediction and decision making in Health Care using Data Mining","volume":"1","author":"Milovic","year":"2012","journal-title":"Kuwait Chapter Arab. 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