ArticleClinical and experimental emergency medicine2018
Predictors of intentional intoxication using decision tree modeling analysis: a retrospective study.
Article in Clinical and experimental emergency medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comparison of machine learning algorithms to predict intentional and unintentional poisoning risk factors.Heliyon · 2023Article
- [Toxicology of suicide cases in the state of Rio Grande do Sul, Brazil, 2017 to 2019Perfil toxicológico de los casos de suicidio en Rio Grande do Sul (Brasil), 2017-2019].Revista panamericana de salud publica = Pan American journal of public health · 2021Article
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4 authors.
Funding
Abstract
objectiveThe suicide rate in South Korea is very high and is expected to increase in coming years. Intoxication is the most common suicide attempt method as well as one of the common reason for presenting to an emergency medical center. We used decision tree modeling analysis to identify predictors of risk for suicide by intentional intoxication.
methodsA single-center, retrospective study was conducted at our hospital using a 4-year registry of the institute from January 1, 2013 to December 31, 2016. Demographic factors, such as sex, age, intentionality, therapeutic adherence, alcohol consumption, smoking status, physical disease, cancer, psychiatric disease, and toxicological factors, such as type of intoxicant and poisoning severity score were collected. Candidate risk factors based on the decision tree were used to select variables for multiple logistic regression analysis.
resultsIn total, 4,023 patients with intoxication were enrolled as study participants, with 2,247 (55.9%) identified as cases of intentional intoxication. Reported annual percentages of intentional intoxication among patients were 628/937 (67.0%), 608/1,082 (56.2%), 536/1,017 (52.7), 475/987 (48.1%) from 2013 to 2016. Significant predictors identified based on decision tree analysis were alcohol consumption, old age, psychiatric disease, smoking, and male sex; those identified based on multiple regression analysis were alcohol consumption, smoking, male sex, psychiatric disease, old age, poor therapeutic adherence, and physical disease.
conclusionWe identified important predictors of suicide risk by intentional intoxication. A specific and realistic approach to analysis using the decision tree modeling technique is an effective method to determine those groups at risk of suicide by intentional intoxication.
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