A Clustering Approach for Predicting Readmissions in Intensive Medicine

Veloso, Rui; Portela, Filipe; Santos, Manuel Filipe; Silva, Álvaro; Rua, Fernando; Abelha, António; Machado, José

doi:10.1016/j.protcy.2014.10.147

Cited by 37 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Data is provided from five data sources: bedside monitor, electronic health record, electronic nursing record, laboratory results, and drugs system. With INTCare it is possible to improve the knowledge base on which health professionals base their decisions [19].…”

Section: Intcarementioning

confidence: 99%

Patients’ Admissions in Intensive Care Units: A Clustering Overview

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract: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.

show abstract

Section: Intcarementioning

confidence: 99%

Patients’ Admissions in Intensive Care Units: A Clustering Overview

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…RuiVelosoa [6] had used the vector quantization method in clustering approach in predicting the readmissions in intensive medicine. The algorithms used invector quantization method are k-means, kmediods and x-means.…”

Section: Clusteringmentioning

confidence: 99%

A novel hybrid outlier detection scheme for health care data mining

Sharma¹

2017

IJITBD

View full text Add to dashboard Cite

show abstract

“…The Intensive Care Unit in CHP employ data mining models to predict patient outcome, readmissions, length of stay and organ failure in real-time, among others [18] [19] [20]. Furthermore, many DM studies have been conducted regarding obstetrics and maternal care, in order to identify services limitations and possible solutions.…”

Section: Knowledge Discovery and Data Mining In Healthcarementioning

confidence: 99%

Predicting Triage Waiting Time in Maternity Emergency Care by Means of Data Mining

Pereira

Torres

Portela

et al. 2016

New Advances in Information Systems and Technologies

Self Cite

View full text Add to dashboard Cite

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

show abstract

A Clustering Approach for Predicting Readmissions in Intensive Medicine

Cited by 37 publications

References 8 publications

Patients’ Admissions in Intensive Care Units: A Clustering Overview

Patients’ Admissions in Intensive Care Units: A Clustering Overview

A novel hybrid outlier detection scheme for health care data mining

Predicting Triage Waiting Time in Maternity Emergency Care by Means of Data Mining

Contact Info

Product

Resources

About