Deep learning neural network derivation and testing to distinguish acute poisonings.
Expert Opin Drug Metab Toxicol
; 19(6): 367-380, 2023.
Article
in En
| MEDLINE
| ID: mdl-37395108
ABSTRACT
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). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied.RESULTS:
There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively).CONCLUSION:
Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https//github.com/ashiskb/npds-workspace.git.Key words
Full text:
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Deep Learning
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Expert Opin Drug Metab Toxicol
Journal subject:
METABOLISMO
/
TOXICOLOGIA
Year:
2023
Document type:
Article
Affiliation country: