A machine learning approach to assess magnitude of asynchrony breathing

Loo, Nien Loong; Chiew, Yeong Shiong; Tan, Chee Pin; Nor, Mohd Basri Mat; Ralib, Azrina

doi:10.1016/j.bspc.2021.102505

Cited by 14 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A classifier is an algorithm that is trained to categorize incoming data into specific classes. Its goal is to identify patterns and correlations in the training set and determine a mapping involving the input feature sets and the output categories [ 3 ].…”

Section: Formation Of Datamentioning

confidence: 99%

See 1 more Smart Citation

Machine learning algorithm for ventilator mode selection, pressure and volume control

T.,

G.,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Mechanical ventilation techniques are vital for preserving individuals with a serious condition lives in the prolonged hospitalization unit. Nevertheless, an imbalance amid the hospitalized people demands and the respiratory structure could cause to inconsistencies in the patient’s inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural approach for generating optimal inhalation strategies. Moreover, machine learning-based classifiers are used to evaluate the precision and performance of the ILC-PID controller. These classifiers able to forecast and choose the perfect type for various inhalation modes, eliminating the likelihood that patients will require mechanical ventilation. In pressure control, the suggested accurate neural categorization exhibited an average accuracy rate of 88.2% in continuous positive airway pressure (CPAP) mode and 91.7% in proportional assist ventilation (PAV) mode while comparing with the other classifiers like ensemble classifier has reduced accuracy rate of 69.5% in CPAP mode and also 71.7% in PAV mode. An average accuracy of 78.9% rate in other classifiers compared to neutral network in CPAP. The neural model had an typical range of 81.6% in CPAP mode and 84.59% in PAV mode for 20 cm H2O of volume created by the neural network classifier in the volume investigation. Compared to the other classifiers, an average of 72.17% was in CPAP mode, and 77.83% was in PAV mode in volume control. Different approaches, such as decision trees, optimizable Bayes trees, naive Bayes trees, nearest neighbour trees, and an ensemble of trees, were also evaluated regarding the accuracy by confusion matrix concept, training duration, specificity, sensitivity, and F1 score.

show abstract

Section: Formation Of Datamentioning

confidence: 99%

“…The off-diagonal components represent the quantity of remarks that were incorrectly classified. People compare the actual class assigned to each instant in the test set to the one given by the trained classifier [ 3 ].…”

Section: Formation Of Datamentioning

confidence: 99%

Machine learning algorithm for ventilator mode selection, pressure and volume control

T.,

G.,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…If q 3 and q 1 are the median of upper half and lower half of the data. Then IQR can be calculated as q 3 -q 1 (11) .…”

Section: Robust Scalingmentioning

confidence: 99%

An Efficient Outlier Detection Using Isolation Forest Based on Robust Scaling and Principal Component Analysis for the Prediction of Anxiety Disorder

Prajesha,

Veni

2023

IJST

View full text Add to dashboard Cite

Objectives: To develop a model for the prediction of anxiety disorder. Presence of outliers may affect the model performance. Hence, here the main consideration is the removal of outliers. Methods: This study proposed an outlier detection method IF-RSPCA in which outliers are handled in two phases. In the first phase, reduce the impact of outliers by using Inter Quartile Range (IQR), then dimensionality reduction is done using principal component analysis. Training time can be reduced by Principal Component Analysis. In the second phase of IF-RSPCA, check the outliers for still exist and removed. Findings: For the performance evaluation two datasets are generated; one using conventional isolation forest and the other using IF-RSPCA. Performance of both datasets are tested using K Neighbors, Decision Tree and Naïve Bayes classifiers, highest accuracies are obtained as 96.10 %,93.80% and 90.2%, respectively. It is found that the dataset generated using proposed method performed well. As compared to previous works on survey datasets, the proposed model is capable of identifying the anxiety, more accurately. Novelty: Conventional isolation forest algorithm is modified using Inter Quartile Range there by improve the performance measures. It helps to removes all the outliers completely. The previous models for the predictions of anxiety disorder are developed without performing the outlier detection; hence chances of misclassification exist. In several previous works, isolation forest combined with clustering, but it is not capable of removing all the outliers. In these cases, outlier handling is performed in a single phase only.

show abstract

“…In particular, this data can be further processed to provide respiratory mechanics and other ventilatory information not available on today’s ventilators, but useful to personalise and guide MV treatment [12] , [13] , [14] , [15] , [16] . CAREDAQ thus provides a platform for future development and integration of software modules, including machine learning models [17] , [18] , [19] or model-based algorithms [14] , [16] , [20] , [21] which could potentially provide real-time, proactive, and patient-specific MV decision support and care, improving care and outcomes.…”

Section: Hardware In Contextmentioning

confidence: 99%