Design of an artificial neural network to predict mortality among COVID-19 patients

Shanbehzadeh, Mostafa; Nopour, Raoof; Kazemi-Arpanahi, Hadi

doi:10.1016/j.imu.2022.100983

Cited by 18 publications

(11 citation statements)

References 71 publications

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“…An artificial neural network (ANN) is a fully connected architecture composed of three layers: input, hidden, and output layers ( Shanbehzadeh et al, 2022 ; Pantic et al, 2023 ), as shown in Figure 3D . The input layer is responsible for receiving data from external sources.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Fuadah,

Qauli,

Marcellinus

et al. 2023

Front. Physiol.

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Introduction: Predicting ventricular arrhythmia Torsade de Pointes (TdP) caused by drug-induced cardiotoxicity is essential in drug development. Several studies used single biomarkers such as qNet and Repolarization Abnormality (RA) in a single cardiac cell model to evaluate TdP risk. However, a single biomarker may not encompass the full range of factors contributing to TdP risk, leading to divergent TdP risk prediction outcomes, mainly when evaluated using unseen data. We addressed this issue by utilizing multi-in silico features from a population of human ventricular cell models that could capture a representation of the underlying mechanisms contributing to TdP risk to provide a more reliable assessment of drug-induced cardiotoxicity.Method: We generated a virtual population of human ventricular cell models using a modified O’Hara-Rudy model, allowing inter-individual variation. IC50 and Hill coefficients from 67 drugs were used as input to simulate drug effects on cardiac cells. Fourteen features (dVmdtrepol, dVmdtmax, Vmpeak, Vmresting, APDtri, APD90, APD50, Capeak, Cadiastole, Catri, CaD90, CaD50, qNet, qInward) could be generated from the simulation and used as input to several machine learning models, including k-nearest neighbor (KNN), Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN). Optimization of the machine learning model was performed using a grid search to select the best parameter of the proposed model. We applied five-fold cross-validation while training the model with 42 drugs and evaluated the model’s performance with test data from 25 drugs.Result: The proposed ANN model showed the highest performance in predicting the TdP risk of drugs by providing an accuracy of 0.923 (0.908–0.937), sensitivity of 0.926 (0.909–0.942), specificity of 0.921 (0.906–0.935), and AUC score of 0.964 (0.954–0.975).Discussion and conclusion: According to the performance results, combining the electrophysiological model including inter-individual variation and optimization of machine learning showed good generalization ability when evaluated using the unseen dataset and produced a reliable drug-induced TdP risk prediction system.

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Section: Methodsmentioning

confidence: 99%

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Fuadah,

Qauli,

Marcellinus

et al. 2023

Front. Physiol.

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“…Shanbehzadeh et al. ( 2022 ) performed proactive prediction on 1710 hospitalized patients records of COVID-19 to help promote the survival chances of hospitalized patients. They proposed two architectures of Artificial neural network (ANN) being used : feed-forward (FF) model with back-propagation (BP) and feed-forward (FF) model with distributed time delay (DTD).…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…These researches applied the ML and DL models to analyzed COVID-19 confirmed cases from hospitals around the world and predict the mortality risk of patients with COVID-19 (Shanbehzadeh et al. 2022 ) and Pourhomayoun and Shakibi ( 2021 ). In fact, these works promote the use of ML and DL algorithms coupled with qualitative data due to it can be useful in the timely and accurate forecasting of COVID-19 patients’ results and will raise patient safety and minimize COVID-19 severity and death.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

COVID-19 vaccine rejection causes based on Twitter people’s opinions analysis using deep learning

Alotaibi

Alomary

Mokni

2023

Soc. Netw. Anal. Min.

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According to the World Health Organization, vaccine hesitancy was one of the ten major threats to global health in 2019, including the COVID-19 vaccine. The availability of vaccines does not always mean utilization. This is because, people have less confidence in vaccines, which resulted in vaccination hesitancy and developing global decline in vaccine intake and has caused viral disease outbreaks worldwide. Therefore, there is a need to understand people’s perceptions about the COVID-19 vaccine to help the manufacturing companies of the vaccine to improve their marketing strategy based on the rejection causes. In this paper, we used multi-class Sentiment Analysis to classify people’s opinions from extracted tweets about COVID-19 vaccines, using firstly different Machine Learning (ML) classifiers such as Logistic Regression (LR), Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbors, Decision Tree (DT), Multinomial Naïve Bayes, Random Forest and Gradient Boosting and secondly various Deep Learning (DL) models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), RNN-LSTM and RNN-GRU. Then, we investigated the analysis of the negative tweets to identify the causes of rejection using the Latent Dirichlet Allocation (LDA) technique. Finally, we classified these negative tweets according to the rejection causes for all the vaccines using the same selected ML and DL models. The result of SA showed that DT gives the best performance with an accuracy of 92.26% and for DL models, GRU achieved 96.83%. Then, we identified five causes: Lack of safety, Side effect, Production problem, Fake news and Misinformation, and Cost. Furthermore, for the classification of the negative tweets according to the identified rejection causes, the LR achieved the best result with an accuracy of 89.97%. For DL models, the LSTM model showed the best result with an accuracy of 91.66%.

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“…Machine learning algorithms utilize previous data as input and generate new predicted values as output. Machine learning algorithms have been employed in numerous domains to solve a wide range of tasks [ 24 , 25 ]. For example, in the comparison of death prediction in hemodialysis patients, the logistic regression method and the random forest model have been compared.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence is being applied in a variety of medical fields nowadays. Machine learning algorithms are being used to speed up disease diagnosis and guide treatment decisions for a wide range of ailments [ 25 – 27 ]. The utilization of artificial intelligence and machine learning in the field of toxicology, particularly clinical toxicology, is relatively new.…”

Section: Introductionmentioning

confidence: 99%

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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Design of an artificial neural network to predict mortality among COVID-19 patients

Cited by 18 publications

References 71 publications

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability

COVID-19 vaccine rejection causes based on Twitter people’s opinions analysis using deep learning

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

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