@article{ref1,
title="Machine learning algorithms to predict seizure due to acute tramadol poisoning",
journal="Human and experimental toxicology",
year="2021",
author="Behnoush, B. and Bazmi, E. and Nazari, S. H. and Khodakarim, S. and Looha, M. A. and Soori, H.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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. <br><br>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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="0960-3271",
doi="10.1177/0960327121991910",
url="http://dx.doi.org/10.1177/0960327121991910"
}