Categorize Readmitted Patients in Intensive Medicine by Means of Clustering Data Mining

Abstract

With a constant increasing in the health expenses and the aggravation of the global economic situation, managing costs and resources in healthcare is nowadays an essential point in the management of hospitals. The goal of this work is to apply clustering techniques to data collected in real-time about readmitted patients in Intensive Care Units in order to know some possible features that affect readmissions in this area. By knowing the common characteristics of readmitted patients it will be possible helping to improve patient outcome, reduce costs and prevent future readmissions. In this study, it was followed the Stability and Workload Index for Transfer (SWIFT) combined with the results of clinical tests for substances like lactic acid, leucocytes, bilirubin, platelets and creatinine. Attributes like sex, age and identification if the patient came from the chirurgical block were also considered in the characterization of potential readmissions. In general, all the models presented very good results being the Davies-Bouldin index lower than 0.82, where the best index was 0.425.

Patients' Admissions in Intensive Care Units: A Clustering Overview

“…Through the predictive feature of data mining techniques, there are projects that allow the anticipation of critical events [4], such as a patient's readmission [5,6], among others. Another possibility is the use of clustering techniques to find patterns in data by creating natural groups with similar characteristics.…”

Real-Time Models to Predict the Use of Vasopressors in Monitored Patients

“…Due to an iterative process, it is now a Pervasive Intelligent Decision Support System (PIDSS) which using Data Mining (DM) supports the decision making process in ICU. This PIDSS is able to predict patient's outcome [12,13], organ failure [14], readmission [15,16], discharge and length of stay [17,18], among others [19]. Data Mining can be define as a process of looking for patterns in great amounts of data, with the intent of describing the data or use it to predict future events.…”

Patients’ Admissions in Intensive Care Units: A Clustering Overview

“…The INTCare system uses data mining techniques to forecast critical events and readmissions [27,28]. One of the studies found also uses clustering techniques to find patterns in the data and assemble them into groups with similar features of patients who were readmitted to the ICU [6].…”

“…Through the predictive feature of data mining techniques, there are projects that allow the anticipation of critical events [4], such as a patient's readmission [5,6], among others. Another possibility…”