Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data

Sierra, Basilio; Serrano, Nicolás; Larrañaga, Pedro; Plasencia, Eliseo; Inza, Iñaki; Jiménez, Juan J.; Revuelta, Pedro; Mora, María L.

doi:10.1016/s0933-3657(00)00111-1

Cited by 50 publications

(25 citation statements)

References 25 publications

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“…Some research used machine learning algorithms, such as artificial neural networks and decision trees as a prediction algorithm in different critical care settings [22][23][24][25][26][27][28]. However, the evaluation of their performance is still under discussion.…”

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

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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

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“…The authors concluded that the performance of the developed expert system is not different from that of the neurosurgeon (expert) and was better than a simple belief network, suggesting that the system can be helpful for the prognostic performance of non-neurosurgeon intensive care unit (ICU) clinicians. Later on, Sierra et al [14] developed a bilevel multi-classifier using Bayesian networks for predicting the survival of ICU patients. The authors concluded that their approach outperformed traditional machine learning methods in the studied problem.…”

Section: Bayesian Network In Intensive Carementioning

confidence: 99%

Using Probabilistic Graphical Models to Enhance the Prognosis of Health-Related Quality of Life in Adult Survivors of Critical Illness

Dias

Granja

Costa‐Pereira

et al. 2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

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Health-related quality of life (HR-QoL) is a subjective concept, reflecting the overall mental and physical state of the patient, and their own sense of well-being. Estimating current and future QoL has become a major outcome in the evaluation of critically ill patients. The aim of this study is to enhance the inference process of 6 weeks and 6 months prognosis of QoL after intensive care unit (ICU) stay, using the EQ-5D questionnaire. The main outcomes of the study were the EQ-5D five main dimensions: mobility, self-care, usual activities, pain and anxiety/depression. For each outcome, three Bayesian classifiers were built and validated with 10-fold cross-validation. Sixty and 473 patients (6 weeks and 6 months, respectively) were included. Overall, 6 months QoL is higher than 6 weeks, with the probability of absence of problems ranging from 31% (6 weeks mobility) to 72% (6 months self-care). Bayesian models achieved prognosis accuracies of 56% (6 months, anxiety/depression) up to 80% (6 weeks, mobility). The prognosis inference process for an individual patient was enhanced with the visual analysis of the models, showing that women, elderly, or people with longer ICU stay have higher risk of QoL problems at 6 weeks. Likewise, for the 6 months prognosis, a higher APACHE II severity score also leads to a higher risk of problems, except for anxiety/depression where the youngest and active have increased risk. Bayesian networks are competitive with less descriptive strategies, improve the inference process by incorporating domain knowledge and present a more interpretable model. The relationships among different factors extracted by the Bayesian models are in accordance with those collected by previous state-of-the-art literature, hence showing their usability as inference model.

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“…As exposed in [30] we have used Bayesian networks as the consensed voting system. Thus, for building the level-1 classifiers, we have used three different Bayesian network structures: naïve Bayes, Interval Estimation Naïve Bayes (IENB) and the idea of Pazzani of joining attributes in naïve Bayes.…”

Section: Level-1 Classifiers Based On Bayesian Networkmentioning

confidence: 99%

Bayesian network multi-classifiers for protein secondary structure prediction

Robles

2004

Artificial Intelligence in Medicine

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Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research.In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average.Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi naïve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.

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Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data

Cited by 50 publications

References 25 publications

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

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

Using Probabilistic Graphical Models to Enhance the Prognosis of Health-Related Quality of Life in Adult Survivors of Critical Illness

Bayesian network multi-classifiers for protein secondary structure prediction

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