Mortality assessment in intensive care units via adverse events using artificial neural networks

Silva, Álvaro; Cortez, Paulo; Santos, Manuel Filipe; Gomes, Lopes; Neves, José

doi:10.1016/j.artmed.2005.07.006

Cited by 56 publications

(44 citation statements)

References 25 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the large amounts of data are still underutilized for the care of critically ill patients in the ICUs. Moreover, considering the unavailability and lack of human experts for various reasons, busy or novice physicians can overlook important details, while automated discovery tools built on various prediction models could analyze the raw data and extract high-level information for the decision maker enabling better decisions [2]. Likewise the ICU setting is particularly well suited for an implementation of a data-driven system which acquires a large quantity of data to discover relationships for diagnostic, prognostic, and therapeutic factors using well-designed, predictive data mining models.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

2011

View full text Add to dashboard Cite

ObjectivesThe intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)'s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model.MethodsThe models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information.ResultsOnly 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871).ConclusionsWith fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

show abstract

Section: Introductionmentioning

confidence: 99%

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

2011

View full text Add to dashboard Cite

show abstract

“…However, some reasons include lack of experts and busy physicians may lead to the elimination of important details while automated prediction methods could analyze the raw data and extract fundamental information for physicians to make a better decision [16]. Moreover, the data collected in the ICUs can be implemented to discover the relation of different types of illness, diagnosis, therapeutic and mortality risk factors.…”

Section: Q2mentioning

confidence: 99%

Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit

Moridani

Setarehdan

Nasrabadi

et al. 2015

Biocybernetics and Biomedical Engineering

View full text Add to dashboard Cite

“…They demonstrated how a data mining technique could be used in developing continuous quality improvement strategy. In literature it is possible to find other studies and applications implementing different data mining techniques using data from different areas in healthcare [13] [14][15] [16]. Some studies show that by accessing the right information, at right time at right place enables taking the right action on time or helps early prevention, while some studies emphasis the importance of quality and availability of data.…”

Section: "Given a Set Of Facts (Data) F A Language L And Some Measumentioning

confidence: 99%

Data Mining on Distributed Medical Databases: Recent Trends and Future Directions

Atilgan

Dogan

2009

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Abstract. As computerization in healthcare services increase, the amount of available digital data is growing at an unprecedented rate and as a result healthcare organizations are much more able to store data than to extract knowledge from it. Today the major challenge is to transform these data into useful information and knowledge. It is important for healthcare organizations to use stored data to improve quality while reducing cost. This paper first investigates the data mining applications on centralized medical databases, and how they are used for diagnostic and population health, then introduces distributed databases. The integration needs and issues of distributed medical databases are described. Finally the paper focuses on data mining studies on distributed medical databases.

show abstract

Mortality assessment in intensive care units via adverse events using artificial neural networks

Abstract: Better prognostic models can be achieved by adopting low cost and real-time intermediate outcomes rather than static data.

Cited by 56 publications

References 25 publications

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

Analysis of heart rate variability as a predictor of mortality in cardiovascular patients of intensive care unit

Data Mining on Distributed Medical Databases: Recent Trends and Future Directions

Contact Info

Product

Resources

About