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.

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-kit-learn 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 precision-recall 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.

A systematic review of clinical and laboratory findings of lead poisoning: lessons from case reports.

Lead is one of the most toxic heavy metals in the environment. The present review aimed to highlight hazardous pollution sources, management, and review symptoms of lead poisonings in various parts of the world. The present study summarized the information available from case reports and case series studies from 2009 to March 2020 on the lead pollution sources and clinical symptoms. All are along with detoxification methods in infants, children, and adults. Our literature compilation includes results from 126 studies on lead poisoning. We found that traditional medication, occupational exposure, and substance abuse are as common as previously reported sources of lead exposure for children and adults. Ayurvedic medications and gunshot wounds have been identified as the most common source of exposure in the United States. However, opium and occupational exposure to the batteries were primarily seen in Iran and India. Furthermore, neurological, gastrointestinal, and hematological disorders were the most frequently occurring symptoms in lead-poisoned patients. As for therapeutic strategies, our findings confirm the safety and efficacy of chelating agents, even for infants. Our results suggest that treatment with chelating agents combined with the prevention of environmental exposure may be an excellent strategy to reduce the rate of lead poisoning. Besides, more clinical studies and long-term follow-ups are necessary to address all questions about lead poisoning management.