Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients

Afrash, Mohammad Reza; Kazemi-Arpanahi, Hadi; Nopour, Raoof; Tabatabaei, Elmira Sadat; Shanbehzadeh, Mostafa

doi:10.18502/jehsd.v7i3.10719

Cited by 3 publications

(3 citation statements)

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“…There were several studies on adult intubation 17 , 25 , 26 . Varzaneh et al 25 predicted the intubation risk of hospitalized coronavirus disease 2019 (COVID-19) patients using a decision tree-based model and showed a reasonable level of accuracy (93%).…”

Section: Discussionmentioning

confidence: 99%

“…We collected datasets of 128 neonates with respiratory difficulties who underwent initial NIV therapy and developed an MDNN model to identify neonates requiring endotracheal intubation and mechanical ventilation within the following 3 h. The proposed model should provide useful information to alert medical staff of a need for intubation occurring within a short time (< 3 h) and reduce persistent monitoring efforts. There were several studies on adult intubation 17,25,26 . Varzaneh et al 25 predicted the intubation risk of hospitalized coronavirus disease 2019 (COVID-19) patients using a decision tree-based model and showed a reasonable level of accuracy (93%).…”

Section: Discussionmentioning

confidence: 99%

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Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network

Park

Kim

et al. 2023

Sci Rep

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Respiratory distress is a common chief complaint in neonates admitted to the neonatal intensive care unit. Despite the increasing use of non-invasive ventilation in neonates with respiratory difficulty, some of them require advanced airway support. Delayed intubation is associated with increased morbidity, particularly in urgent unplanned cases. Early and accurate prediction of the need for intubation may provide more time for preparation and increase safety margins by avoiding the late intubation at high-risk infants. This study aimed to predict the need for intubation within 3 h in neonates initially managed with non-invasive ventilation for respiratory distress during the first 48 h of life using a multimodal deep neural network. We developed a multimodal deep neural network model to simultaneously analyze four time-series data collected at 1-h intervals and 19 variables including demographic, physiological and laboratory parameters. Evaluating the dataset of 128 neonates with respiratory distress who underwent non-invasive ventilation, our model achieved an area under the curve of 0.917, sensitivity of 85.2%, and specificity of 89.2%. These findings demonstrate promising results for the multimodal model in predicting neonatal intubation within 3 h.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network

Park

Kim

et al. 2023

Sci Rep

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“…Thus, ML-based prediction models can significantly contribute to triaging hazardous patients and allocating the limited hospital resources for mortality risk prediction [72,73], resulting in reducing uncertainty by quantitative, objective, and evidence-based models for risk classification. Furthermore, the ML provides a better strategy for physicians to reduce complications and improve patient survival [74][75][76][77].…”

Section: Discussionmentioning

confidence: 99%

Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms

2022

J Biomed Phys Eng

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Background: Since hospitalized patients with COVID-19 are considered at high risk of death, the patients with the sever clinical condition should be identified. Despite the potential of machine learning (ML) techniques to predict the mortality of COVID-19 patients, high-dimensional data is considered a challenge, which can be addressed by metaheuristic and nature-inspired algorithms, such as genetic algorithm (GA).Objective: This paper aimed to compare the efficiency of the GA with several ML techniques to predict COVID-19 in-hospital mortality. Material and Methods:In this retrospective study, 1353 COVID-19 in-hospital patients were examined from February 9 to December 20, 2020. The GA technique was applied to select the important features, then using selected features several ML algorithms such as K-nearest-neighbor (K-NN), Decision Tree (DT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) were trained to design predictive models. Finally, some evaluation metrics were used for the comparison of developed models.Results: A total of 10 features out of 56 were selected, including length of stay (LOS), age, cough, respiratory intubation, dyspnea, cardiovascular diseases, leukocytosis, blood urea nitrogen (BUN), C-reactive protein, and pleural effusion by 10-independent execution of GA. The GA-SVM had the best performance with the accuracy and specificity of 9.5147e+01 and 9.5112e+01, respectively. Conclusion:The hybrid ML models, especially the GA-SVM, can improve the treatment of COVID-19 patients, predict severe disease and mortality, and optimize the utilization of health resources based on the improvement of input features and the adaption of the structure of the models.

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Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms

Rahimi,

Afrash,

Shadnia

et al. 2024

BMC Med Inform Decis Mak

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Background Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. Methods Using a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score. Result The study comprised 980 patients in total. The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%. Conclusion ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.

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Proposing an Intelligent Monitoring System for Early Prediction of Need for Intubation among COVID-19 Hospitalized Patients

Cited by 3 publications

References 0 publications

Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network

Predicting the need for intubation within 3 h in the neonatal intensive care unit using a multimodal deep neural network

Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms

Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms

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