Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System.
Basic Clin Pharmacol Toxicol
; 131(6): 566-574, 2022 Dec.
Article
en En
| MEDLINE
| ID: mdl-36181236
ABSTRACT
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. There were 201 031 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.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Intoxicación
/
Venenos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Adolescent
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Adult
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Aged
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Aged80
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Child
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Child, preschool
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Humans
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Infant
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Middle aged
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Newborn
Idioma:
En
Revista:
Basic Clin Pharmacol Toxicol
Asunto de la revista:
FARMACOLOGIA
/
TOXICOLOGIA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos