Bayesian Decision Trees for EEG Assessment of newborn brain maturity

Jakaite, Livia; Schetinin, Vitaly; Maple, Carsten; Schult, Joachim

doi:10.1109/ukci.2010.5625584

Cited by 27 publications

(9 citation statements)

References 9 publications

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“…This allowed us to hypothesise that these variables made a weak contribution to the problem. We removed DT models which such variables from the ensemble, and observed a decrease in the uncertainty of the predictive density [20,31].…”

Section: Bayesian Predictionsmentioning

confidence: 99%

Bayesian Decision Trees for predicting survival of patients: A study on the US National Trauma Data Bank

Schetinin

Jakaite

Jakaitis

et al. 2013

Computer Methods and Programs in Biomedicine

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Trauma and Injury Severity Score (TRISS) models have been developed for predicting the survival probability of injured patients the majority of which obtain up to three injuries in six body regions. Practitioners have noted that the accuracy of TRISS predictions is unacceptable for patients with a larger number of injuries. Moreover, the TRISS method is incapable of providing accurate estimates of predictive density of survival, that are required for calculating confidence intervals. In this paper we propose Bayesian inference for estimating the desired predictive density. The inference is based on decision tree models which split data along explanatory variables, that makes these models interpretable. The proposed method has outperformed the TRISS method in terms of accuracy of prediction on the cases recorded in the US National Trauma Data Bank. The developed method has been made available for evaluation purposes as a stand-alone application.

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Section: Bayesian Predictionsmentioning

confidence: 99%

Bayesian Decision Trees for predicting survival of patients: A study on the US National Trauma Data Bank

Schetinin

Jakaite

Jakaitis

et al. 2013

Computer Methods and Programs in Biomedicine

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“…At the most basic level, EEG datasets consist of 2D (time and channel) matrices of real values representing brain-generated potentials recorded on the scalp in relation to specific task conditions [ 39 ]. A great number of traditional machine learning algorithms such as logistic regression [ 40 ], SVM [ 41 ], decision tree [ 42 ], and random forest [ 43 ] have been applied on EEG data. In the clinical setting, EEG signals combined with machine learning have recently been used for identifying sleep disorders, epilepsy, strokes, and other neurological disorders [ 44 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

et al. 2022

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(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

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“…For instance, techniques like CNN are utilised for classifying Chest-X-rays in order to determine whether pneumonia is present. Some of the exciting research has been done in areas like abnormal-patterns detection [6][7][8][9][10][11][12][13], biometric recognition [14,15], trauma seriousness valuation [16][17][18][19], accident prevention at the 1 3 airport [20], predicting efficiency in information using ANN [21] and diagnoses of bone pathology [22]. However, the higher divergence in the image features impacts the retrieval accuracy [1].…”

Section: Introductionmentioning

confidence: 99%

Review on Pneumonia Image Detection: A Machine Learning Approach

Kareem

Liu

Sant

2022

Hum-Cent Intell Syst

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This paper surveys and examines how computer-aided techniques can be deployed in detecting pneumonia. It also suggests a hybrid model that can effectively detect pneumonia while using the real-time medical image data in a privacy-preserving manner. This paper will explore how various preprocessing techniques such as X-rays can detect and classify multiple diseases. The survey also examines how different machine learning technologies like convolution neural network (CNN), k-nearest neighbor (KNN), RESNET, CheXNet, DECNET and artificial neural network (ANN) can be used in detecting pneumonia disease. In this article, we have performed a comprehensive review of the literature to find how we can combine hospitals and medical institutions to train the machine learning models from their datasets so that the ML algorithms can detect disease more efficiently and correctly. We have proposed the future work of using transfer learning combined with federated knowledge that could help the medical institutions and hospitals form a combined approach of performing medical image detection using real-time datasets. We have also explored the scope, future work and limitations of the proposed solution.

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Bayesian Decision Trees for EEG Assessment of newborn brain maturity

Cited by 27 publications

References 9 publications

Bayesian Decision Trees for predicting survival of patients: A study on the US National Trauma Data Bank

Bayesian Decision Trees for predicting survival of patients: A study on the US National Trauma Data Bank

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

Review on Pneumonia Image Detection: A Machine Learning Approach

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