Prognostic factors of acetaminophen exposure in the United States: An analysis of 39,000 patients

Mehrpour, Omid; Saeedi, Farhad; Hadianfar, Ali; Mégarbane, Bruno; Hoyte, Christopher

doi:10.1177/09603271211061503

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…Moreover, AI can aid physicians with their clinical decision-making and to predict the outcome [ 13 , 16 ]. In addition, its performance in the field of medical toxicology received much attention recently [ 17 – 19 ]. However, it is important to understand how AI fits into large heterogeneous datasets and functions.…”

Section: Introductionmentioning

confidence: 99%

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

Mehrpour

Saeedi

Hoyte

et al. 2022

BMC Pharmacol Toxicol

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Background With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin’s toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT). Methods This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation. Results Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively. Conclusions In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases.

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Section: Introductionmentioning

confidence: 99%

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

Mehrpour

Saeedi

Hoyte

et al. 2022

BMC Pharmacol Toxicol

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“…18 The use of decision tree algorithms in the emergency department and medical toxicology has received considerable attention in recent years. [19][20][21][22][23] However, there is currently no practical system to aid clinicians in accurately predicting outcomes of bupropion exposure cases based on the clinical features.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, it uses a risk stratification process to develop an overall risk of disease that simultaneously relies on various criteria 18 . The use of decision tree algorithms in the emergency department and medical toxicology has received considerable attention in recent years 19–23 . However, there is currently no practical system to aid clinicians in accurately predicting outcomes of bupropion exposure cases based on the clinical features.…”

Section: Introductionmentioning

confidence: 99%

The role of decision tree and machine learning models for outcome prediction of bupropion exposure: A nationwide analysis of more than 14 000 patients in the United States

Mehrpour

Saeedi

Vohra

et al. 2023

Basic Clin Pharma Tox

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Bupropion is widely used for the treatment of major depressive disorder and for smoking cessation assistance. Unfortunately, there are no practical systems to assist clinicians or poison centres in predicting outcomes based on clinical features. Hence, the purpose of this study was to use a decision tree approach to inform early diagnosis of outcomes secondary to bupropion overdose. This study utilized a dataset from the National Poison Data System, a 6-year retrospective cohort study on toxic exposures and patient outcomes. A machine learning algorithm (decision tree) was applied to the dataset using the sci-kitlearn library in Python. Shapley Additive exPlanations (SHAP) were used as an explainable method. Comparative analysis was performed using random forest (RF), Gradient Boosting classification, eXtreme Gradient Boosting, Light Gradient Boosting (LGM) and voting ensembling. ROC curve and precisionrecall curve were used to analyse the performance of each model. LGM and RF demonstrated the highest performance to predict outcome of bupropion exposure. Multiple seizures, conduction disturbance, intentional exposure, and confusion were the most influential factors to predict the outcome of bupropion exposure. Coma and seizure, including single, multiple and status, were the most important factors to predict major outcomes.

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Anti-inflammatory and antipyretic analgesics and drugs used in gout

Long

Olah²

2022

Side Effects of Drugs Annual

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Prognostic factors of acetaminophen exposure in the United States: An analysis of 39,000 patients

Cited by 5 publications

References 48 publications

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

Utility of support vector machine and decision tree to identify the prognosis of metformin poisoning in the United States: analysis of National Poisoning Data System

The role of decision tree and machine learning models for outcome prediction of bupropion exposure: A nationwide analysis of more than 14 000 patients in the United States

Anti-inflammatory and antipyretic analgesics and drugs used in gout

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