AIMC Topic: Poison Control Centers

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Predictive modeling of methadone poisoning outcomes in children ≤ 5 years: utilizing machine learning and the National Poison Data System for improved clinical decision-making.

European journal of pediatrics Feb 11, 2025
UNLABELLED: The escalating therapeutic use of methadone has coincided with an increase in accidental ingestions, particularly among children ≤ 5 years. This study utilized machine learning (ML) methodologies on data from the National Poison Data Syst...
Humans Machine Learning Retrospective Studies Clinical Decision-Making Female Male Infant Child, Preschool Methadone Poison Control Centers
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Toxicovigilance 2.0 - modern approaches for the hazard identification and risk assessment of toxicants in human beings: A review.

Toxicology Feb 16, 2024
The attempt to define toxicovigilance can be based on defining its fundamental principles: prevention of infections with toxic substances, collecting information on poisonings, both in terms of their sources and side effects, and confirming poisoning...
Humans Artificial Intelligence Animals Risk Assessment Poison Control Centers
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Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Drug and chemical toxicology Nov 9, 2023
Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utiliz...
Humans Machine Learning Databases, Factual Retrospective Studies Female Reproducibility of Results Young Adult Adult Male Middle Aged Adolescent United States Longitudinal Studies Analgesics, Opioid Methadone Poison Control Centers
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