| Autor: |
Mary R. Lee, Vignesh Sankar, Aaron Hammer, William G. Kennedy, Jennifer J. Barb, Philip G. McQueen, Lorenzo Leggio |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
EClinicalMedicine, Vol 12, Iss , Pp 70-78 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2589-5370 |
| DOI: |
10.1016/j.eclinm.2019.05.008 |
| Popis: |
Objective: The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD) seeks treatment. Method: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADT's were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models. Results: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy. Interpretation: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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