Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures

Background This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm. Methods We conducted a retrospective cohort study using the National Poison Data System (NPDS). All patients w...

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Vydané v:BMC medical informatics and decision making Ročník 23; číslo 1; s. 102 - 7
Hlavní autori: Mehrpour, Omid, Hoyte, Christopher, Nakhaee, Samaneh, Megarbane, Bruno, Goss, Foster
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 01.06.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Accuracy
Acetaminophen
Acetaminophen - adverse effects
Acute acetaminophen poisoning
Algorithms
Analgesics
Analgesics, Non-Narcotic
Analysis
APAP
Classification
Complications and side effects
Data systems
Decision making
Decision Tree
Decision Trees
Drowsiness
Drug dosages
Exposure
Health Informatics
Human subjects
Humans
Information Systems and Communication Service
Ingestion
Laboratories
Lethargy
Liver
Machine learning
Management of Computing and Information Systems
Medicine
Medicine & Public Health
Model accuracy
Mortality
NPDS
Patient outcomes
Patients
Performance evaluation
Poisoning
Poisons
Repeated supra-therapeutic ingestion
Retrospective Studies
Vomiting
United States
United States > US
APAP
Decision Tree
Acute acetaminophen poisoning
NPDS
Repeated supra-therapeutic ingestion
ISSN:1472-6947, 1472-6947
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Shrnutí:Background This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm. Methods We conducted a retrospective cohort study using the National Poison Data System (NPDS). All patients with RSTI acetaminophen exposure (n = 4,522) between January 2012 and December 2017 were included. Additionally, 4,522 randomly selected acute acetaminophen ingestion cases were included. After that, the DT machine learning algorithm was applied to differentiate acute acetaminophen exposure from supratherapeutic exposures. Results The DT model had accuracy, precision, recall, and F1-scores of 0.75, respectively. Age was the most relevant variable in predicting the type of acetaminophen exposure, whether RSTI or acute. Serum aminotransferase concentrations, abdominal pain, drowsiness/lethargy, and nausea/vomiting were the other most important factors distinguishing between RST and acute acetaminophen exposure. Conclusion DT models can potentially aid in distinguishing between acute and RSTI of acetaminophen. Further validation is needed to assess the clinical utility of this model.
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ISSN:1472-6947
1472-6947
DOI:10.1186/s12911-023-02188-2