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Patients’ Admissions in Intensive Care Units: A Clustering Overview

Centro ALGORITMI, University of Minho, Campus Azurém, 4800-058 Guimarães, Portugal
Intensive Care Unit, Centro Hospitalar do Porto, Largo do Prof. Abel Salazar, 4099-001 Porto, Portugal
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Information 2017, 8(1), 23;
Received: 20 November 2016 / Revised: 13 February 2017 / Accepted: 14 February 2017 / Published: 17 February 2017
PDF [507 KB, uploaded 17 February 2017]


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 × 10−17 and a Davies–Bouldin index of −0.652. View Full-Text
Keywords: data mining; decision support systems; clustering; intensive care; admissions; INTCare system data mining; decision support systems; clustering; intensive care; admissions; INTCare system

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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MDPI and ACS Style

Ribeiro, A.; Portela, F.; Santos, M.; Abelha, A.; Machado, J.; Rua, F. Patients’ Admissions in Intensive Care Units: A Clustering Overview. Information 2017, 8, 23.

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