{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T07:21:30Z","timestamp":1789888890963,"version":"4.0.1"},"reference-count":45,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T00:00:00Z","timestamp":1612396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hum Exp Toxicol"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013\u20132019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Na\u00efve 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>A perfect prediction model may help improve clinicians\u2019 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/0960327121991910","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T02:12:18Z","timestamp":1612836738000},"page":"1225-1233","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine learning algorithms to predict seizure due to acute tramadol poisoning"],"prefix":"10.1177","volume":"40","author":[{"given":"B","family":"Behnoush","sequence":"first","affiliation":[{"name":"Department of Forensic Medicine, Tehran University of Medical Sciences, Tehran, 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