Patients' Admissions in Intensive Care Units: A Clustering Overview

Ana Ribeiro, Filipe Portela, Manuel Santos, António Abelha, José Machado, Fernando Rua
<span title="2017-02-17">2017</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="" style="color: black;">Information</a> </i> &nbsp;
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
more &raquo; ... es, 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. ) and different evaluation methods: silhouette, inter-cluster distance, and distance to centroids, K-means, and Davies-Bouldin index. Clustering techniques were used in this work, which originated from a prior application of classification techniques. In addition, it allowed identifying some useful variables for the application of classification techniques [7] . The data used are the same before admission. It was intended to create patterns that that allow understanding of the patient condition at admission (using data collected during the patient stay in hospital). This situation allows the clinician to easily understand if a patient has, or does not have, similar values that were presented by the clusters created. These data are stored in the electronic health record during patient admission in other services. The values are combined and humans cannot make all of the combinations without the use of a machine. Another goal of this work is alerting the clinicians to the patient's condition. For example, the patient can have five days before ICU admission due to transplant or surgery. These data are stored in the database and, when the patient is admitted to the ICU, they are considered. When a patient is outside the ICU and has some of these values, the clinician will be alerted when there is a match of some patient conditions (outside the ICU) with another patient admitted earlier to the ICU. All of this work is framed in INTCare research work. Several studies were performed using other tools and algorithms. This paper presents a particular part of that research work. The best scenario used only three attributes, since the others had a negative impact on the results. The best results had a silhouette of 1, inter-cluster distance of 1.5, and distance to centroids of 6.2 × 10 −17 . This document is divided into five sections. The first is the introduction of the problem and the topics that will be discussed during the paper are described. The second section provides a background, presenting the basic concepts involved in this work. The third section is the Study Description where the tools and techniques used in this work are identified, and the business understanding, data understanding, and preparation, modeling, and evaluation are presented. The fourth section is a discussion of the findings. This section presents some interesting analytical points about the results achieved within this work. The fifth section provides the achieved conclusions, with the results obtained, and where further work is introduced.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3390/info8010023</a> <a target="_blank" rel="external noopener" href="">fatcat:ryfo34qxt5fkbckpgysfhooy6q</a> </span>
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