Patients’ Admissions in Intensive Care Units: A Clustering Overview

Ribeiro, Ana; Portela, Filipe; Santos, Manuel Filipe; Abelha, António; Machado, José Manuel; Rua, Fernando

doi:10.3390/info8010023

Cited by 8 publications

(2 citation statements)

References 24 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, DM is a multidisciplinary area that incorporates mathematical functions, Machine Learning techniques, and statistical analysis to uncover hidden patterns or rules and extract previously unknown and potentially meaningful knowledge [36]. DM techniques include descriptive algorithms, for finding interesting patterns in the data, like associations, clusters, and subgroups [37], and predictive algorithms, that perform induction to make predictions of a specific attribute, which results in models that can be used for regression and classification [36,38].…”

Section: Methodsmentioning

confidence: 99%

Recommendation System Using Autoencoders

et al. 2020

Self Cite

View full text Add to dashboard Cite

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

show abstract

Section: Methodsmentioning

confidence: 99%

Recommendation System Using Autoencoders

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

Patients' Admissions in Intensive Care Units: A Clustering Overview

Ribeiro

Portela

Santos

et al. 2016

2016 IEEE 18th Conference on Business Informatics (CBI)

Self Cite

View full text Add to dashboard Cite

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