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In Basic & clinical pharmacology & toxicology

The primary aim of this pilot study was to develop a machine learning algorithm to predict and distinguish eight poisoning agents based on clinical symptoms. Data were used from the National Poison Data System from 2014 to 2018, for patients 0-89 years old with single-agent exposure to 8 drugs or drug classes (acetaminophen, aspirin, benzodiazepines, bupropion, calcium channel blockers, diphenhydramine, lithium, and sulfonylureas). Four classifier prediction models were applied to the data: Logistic regression, LightGBM, XGBoost, and CatBoost. There were 201031 cases used to develop and test the algorithms. Among the four models, accuracy ranged 77-80%, with precision and F1 scores of 76-80%, and recall of 77-78%. Overall specificity was 92% for all models. Accuracy was highest for identifying sulfonylureas, acetaminophen, benzodiazepines, and diphenhydramine poisoning. F1 scores were highest for correctly classifying sulfonylureas, acetaminophen, and benzodiazepine poisonings. Recall was highest for sulfonylureas, acetaminophen, and benzodiazepines, and lowest for bupropion. Specificity was >99% for models of sulfonylureas, calcium channel blockers, lithium, and aspirin. For single-agent poisoning cases among the eight possible exposures, machine learning models based on clinical signs and symptoms moderately predicted the causal agent. CatBoost and LightGBM classifier models had the highest performance of those tested.

Mehrpour Omid, Hoyte Christopher, Delva-Clark Heather, Al Masud Abdullah, Biswas Ashis, Schimmel Jonathan, Nakhaee Samaneh, Goss Foster


Clinical decision aid, Machine learning, NPDS, Overdose, Poisoning, Toxicology