Machine learning algorithms to predict seizure due to acute tramadol poisoning

Abstract

Introduction: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making. Methods: Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013–2019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) algorithms. Area Under the Curve (AUC) and other diagnostic criteria were used to assess performance of models. Results: In 909 patients, 544 (59.8%) experienced seizures. The important predictors of seizure were sex, pulse rate, arterial blood oxygen pressure, blood bicarbonate level and pH. SVM (AUC = 0.68), NB (AUC = 0.71) and ANN (AUC = 0.70) models outperformed k-NN model (AUC = 0.58). NB model had a higher sensitivity and negative predictive value and k-NN model had higher specificity and positive predictive values than other models. Conclusion: A perfect prediction model may help improve clinicians’ decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches

“…Behnoush et al's [ 52 ] review was intended to improve and assess machine learning algorithms to forecast seizures problems, recognize critical patients, and enable suitable clinical directives. Emergency department collects the data of numerous features of acute problem cases.…”

“…The famous cross-validation and general exercise approach is tenfold cross-validation [ 51 ]. In each tenfold, one flat section of the dataset is reflected as testing data used for the testing model, and the leftover nine parts are used as the training data [ 52 ]. The performance of most classifiers, in general, is measured in terms of precision, recall, and F -measure [ 53 ].…”

Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System

“…15 Dong et al investigated ML to predict opioid overdose, and found high recall using a random forest model and high accuracy with deep learning models. 22 Other studies have leveraged ML to predict paraquat poisoning prognosis, 23,24 seizures from tramadol poisoning, 25 adverse drug events in elderly patients, 26 smoking cessation treatment outcome, 27 lead poisoning in children, 28 pesticide ototoxicity 29 and inadequate medication responses in the emergency department. 30 In recent years, ML in medicine has garnered considerable interest, from anticipated cost-effectiveness, speed, and accuracy.…”

Machine learning‐based prediction of 1‐year mortality in hypertensive patients undergoing coronary revascularization surgery

“…Feature selection is widely applied to removing irrelevant and unnecessary data, thereby could improve the accuracy and understanding of the ML models. 43 RF algorithm has been applied in many studies [44][45][46] and found to perform better in classification prediction modeling compared to other methods in ML techniques. 47,48 Since the use of too many features can lead to a decrease in the model's performance, reducing the number of variables and taking the correlation of features into consideration are among the advantages of the RF model.…”

“…47,48 Since the use of too many features can lead to a decrease in the model's performance, reducing the number of variables and taking the correlation of features into consideration are among the advantages of the RF model. 45 Likewise, in this study, we used the RF feature selector technique to determine the top features. Based on our results, the ventilation time after the surgery was recorded as the most influential variable for predicting mortality, followed by baseline EF.…”