Decisions, Decisions: Machine Learning as a Tool to Identify Alcohol-use Disorder Treatment Seekers
Citation:
Whelan, R., Decisions, Decisions: Machine Learning as a Tool to Identify Alcohol-use Disorder Treatment Seekers, NeuroImage: Clinical 22, 2019Download Item:
Abstract:
Alcohol use disorders (AUD) are very common in the developed world [1], yet only a minority of individuals with AUD seek treatment. Several factors influence the choice to seek treatment, including demographic, psychological and physical impediments. Integrating information from a number of disparate data sources is challenging. In this issue of EClinicalMedicine, Lee et al. [2] report a machine learning analysis that classified individuals with AUD as either treatment seekers or non-seekers. Notable strengths of this study included the examination of a wide range of predictor variables, the application of an innovative data analysis method (alternating decision trees; ADTs), and the use of an external validation sample to quantify reproducibility. There are, however, caveats that apply to the use of machine-learning methods in biomedical research.
URI:
https://www.sciencedirect.com/science/article/pii/S2589537019301051?via%3Dihubhttp://hdl.handle.net/2262/89522
Author's Homepage:
http://people.tcd.ie/whelanr3
Author: Whelan, Robert
Publisher:
ElsevierType of material:
Journal ArticleURI:
https://www.sciencedirect.com/science/article/pii/S2589537019301051?via%3Dihubhttp://hdl.handle.net/2262/89522
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EClinicalMedicine;Availability:
Full text availableKeywords:
Alcohol use disorders (AUD), Adolescents, Resting state, Personality, Genome, Co-developmentDOI:
http://dx.doi.org/10.1016/j.eclinm.2019.06.012Metadata
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