Deep learning neural network derivation and testing to distinguish acute poisonings.

Introduction: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs.

Research Design & Methods: Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium).

Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System.

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 eight 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.

Pediatric Fatalities Associated With Over-the-Counter Cough and Cold Medications.

Background And Objectives: In 2008, over-the-counter cough and cold medications (CCMs) underwent labeling changes in response to safety concerns, including fatalities, reported in children exposed to CCMs. The objective of this study is to describe fatalities associated with exposures to CCMs in children <12 years old that were detected by a safety surveillance system from 2008 to 2016.

Methods: Fatalities in children <12 years old that occurred between 2008 and 2016 associated with oral exposure to one or more CCMs were identified by the Pediatric Cough and Cold Safety Surveillance System.

Impact of product safety changes on accidental exposures to liquid laundry packets in children.

Objectives: To evaluate the impact of the ASTM International (formerly American Society of Testing Materials) safety standard and associated product safety changes on accidental exposures to liquid laundry packets (LLPs) in children.

Methods: The National Poison Data System was queried for reports of accidental exposures to LLPs in children <6 years old received from 01 July 2012 to 31 December 2018. In 2014, ASTM International began developing a standard specifying voluntary product changes to reduce the risk of LLP exposures in young children.

Flow Restrictors and Reduction of Accidental Ingestions of Over-the-Counter Medications.

Introduction: Flow restrictors are child-resistant packaging innovations designed to limit the amount of liquid dispensed from a medication bottle. In 2011, flow restrictors were added to pediatric liquid single-ingredient acetaminophen formulations. The hypothesis of this study is that implementation would be associated with reduced volume and severity of pediatric acetaminophen exposures reported to the U.