Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks

Arizmendi, Carlos; Romero, Enrique; Alquézar, René; Caminal, P.; Díaz, Iván; Benito, S.; Giraldo, Beatriz F.

doi:10.1109/iembs.2009.5332742

Cited by 10 publications

(18 citation statements)

References 13 publications

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“…Although they cannot deal with missing data, ANNs can simultaneously handle numerous variables by building models with reference to outliers and nonlinear interactions among variables. [4][5][6] Whereas conventional statistical methods reveal parameters that are significant only for the overall population, ANNs include parameters that are significant at the individual level even if they are not significant for the overall population. Unlike other standard statistical tests, ANNs can also manage complexity even when the sample size is small and even when the ratio between variables and records is unbalanced.…”

Section: Discussionmentioning

confidence: 99%

“…Unlike other standard statistical tests, ANNs can also manage complexity even when the sample size is small and even when the ratio between variables and records is unbalanced. [4][5][6] That is, ANNs avoid the dimensionality problem. The large and homogeneous data set in this study enabled robust network training because all clinical variables had shown potential effects on mortality in previous logistic regression models.…”

Section: Discussionmentioning

confidence: 99%

“…First, the clinical picture obtained in this analysis of claims data is not as precise as that of a prospective analysis of racial differences, and other variables that have uncertain effects on clinical outcomes. [4][5][6] The analysis is not limited to clinical parameters. Other variables can be evaluated for use in improving the predictive accuracy of the model.…”

Section: Discussionmentioning

confidence: 99%

“…The ANN architecture proposed here can also include more than one dependent variable and can perform a nonlinear transformation between dependent variables. [4][5][6] Future studies may evaluate the effectiveness of the proposed architecture for analyzing other characteristics of patients or clinical characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Once a model is trained, prediction outputs can be generated from novel records. [4][5][6] Previous comparisons of logistic regression and ANN models for predicting outcomes of mechanical ventilation have shown major shortcomings. [7][8][9][10] First, few have used pooled data analysis for more than 2 years.…”

mentioning

confidence: 99%

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In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Chen

Shi

Lee

et al. 2013

American Journal of Critical Care

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Background Few studies have used pooled data for more than 2 years and few have analyzed data for patients receiving mechanical ventilation in Taiwan. Objective To validate the use of an artificial neural network model for predicting in-hospital mortality in patients receiving mechanical ventilation in Taiwan and to compare the predictive accuracy of the artificial neural network model with that of a logistic regression model. Methods Retrospective comparison of 1000 pairs of data sets processed by logistic regression and artificial neural network models based on initial clinical data for 213 945 patients receiving mechanical ventilation. For each pair of artificial neural network and logistic regression models, the area under the receiver operating characteristic curves, Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared by using t tests. Global sensitivity analysis and sensitivity score approach were also used to assess the relative significance of input parameters in the system model and the relative importance of variables. Results Compared with the logistic regression model, the artificial neural network model had a better accuracy rate in 96.3% of cases, better Hosmer-Lemeshow statistics in 41.2% of cases, and a better area under the curve in 97.6% of cases. Hospital volume was the most influential (sensitive) variable affecting in-hospital mortality, followed by Charlson comorbidity index, length of stay, and hospital type. Conclusions Compared with the conventional logistic regression model, the artificial neural network model was more accurate in predicting in-hospital mortality and had higher overall performance indices.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

Chen

Shi

Lee

et al. 2013

American Journal of Critical Care

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The efficacy and effectiveness of machine learning for weaning in mechanically ventilated patients at the intensive care unit: a systematic review

et al. 2018

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Weaning from mechanical ventilation in the intensive care unit (ICU) is a complex clinical problem and relevant for future organ engineering. Prolonged mechanical ventilation (MV) leads to a range of medical complications that increases length of stay and costs as well as contributes to morbidity and even mortality and long-term quality of life. The need to reduce MV is both clinical and economical. Artificial intelligence or machine learning (ML) methods are promising opportunities to positively influence patient outcomes. ML methods have been proposed to enhance clinical decisions processes by using the large amount of digital information generated in the ICU setting. There is a particular interest in empirical methods (such as ML) to improve management of "difficult-to-wean" patients, due to the associated costs and adverse events associated with this population. A systematic literature search was performed using the OVID, IEEEXplore, PubMed, and Web of Science databases. All publications that included (1) the application of ML to weaning from MV in the ICU and (2) a clinical outcome measurement were reviewed. A checklist to assess the study quality of medical ML publications was modified to suit the critical assessment of ML in MV weaning literature. The systematic search identified nine studies that used ML for weaning management from MV in critical care. The weaning management application areas included (1) prediction of successful spontaneous breathing trials (SBTs), (2) prediction of successful extubation, (3) prediction of arterial blood gases, and (4) ventilator setting and oxygenation-adjustment advisory systems. Seven of the nine studies scored seven out of eight on the quality index. The remaining two of the nine studies scored one out of eight on the quality index. This scoring may, in part, be explained by the publications' focus on technical novelty, and therefore focusing on issues most important to a technical audience, instead of issues most important for a systematic medical review. This review showed that only a limited number of studies have started to assess the efficacy and effectiveness of ML for MV in the ICU. However, ML has the potential to be applied to the prediction of SBT failure, extubation failure, and blood gases, and also the adjustment of ventilator and oxygenation settings. The available databases for the development of ML in this clinical area may still be inadequate. None of the reviewed studies reported on the procedure, treatment, or sedation strategy undergone by patients. Such information is unlikely to be required in a technical publication but is potentially vital to the development ML techniques that are sufficiently robust to meet the needs of the "difficult-to-wean" patient population.

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Weaning outcome prediction from heterogeneous time series using Normalized Compression Distance and Multidimensional Scaling

Lillo-Castellano

Mora-Jiménez

Santiago-Mozos

et al. 2013

Expert Systems with Applications

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In the Intensive Care Unit of a hospital (ICU), weaning can be defined as the process of gradual reduction in the level of mechanical ventilation support. A failed weaning increases the risk of death in prolonged mechanical ventilation patients. Different methods for weaning outcome prediction have been proposed using variables and time series extracted from the monitoring systems, however, monitored data are often non-regularly sampled, hence limiting its use in conventional automatic prediction systems. In this work, we propose the joint use of two statistical techniques, Normalized Compression Distance (NCD) and Multidimensional Scaling (MDS), to deal with data heterogeneity in monitoring systems for weaning outcome prediction. A total of 104 weanings were selected from 93 patients under mechanical ventilation from the ICU of Hospital Universitario Fundación Alcorcón; for each weaning, time series (TS), clinical laboratory and general descriptors variables were collected during 48 hours previous to the moment of withdrawal mechanical support (extubation). The TS diastolic blood pressure variable provided the best weaning prediction, with an

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Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks

Cited by 10 publications

References 13 publications

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

The efficacy and effectiveness of machine learning for weaning in mechanically ventilated patients at the intensive care unit: a systematic review

Weaning outcome prediction from heterogeneous time series using Normalized Compression Distance and Multidimensional Scaling

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