A machine learning model for the prediction of unhealthy alcohol use among women of childbearing age in Alabama.
Introduction: This study utilizes a machine learning model to predict unhealthy alcohol use treatment levels among women of childbearing age. Methods: In this cross-sectional study, women of childbearing age (n = 2397) were screened for alcohol use over a 2-year period as part of the AL-SBIRT (scree...
| Publicado en: | Alcohol & Alcoholism Vol. 59; no. 2; pp. 1 - 8 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176004704&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176004704 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07350414 FA3 jtl: Alcohol & Alcoholism issn: 07350414 maglogo: N pubinfo: dt: Mar2024 vid: 59 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 176004704 176004704 176004704 10.1093/alcalc/agad075 176004704 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A machine learning model for the prediction of unhealthy alcohol use among women of childbearing age in Alabama. aug: au: Johnson, Karen A McDaniel, Justin T Okine, Joana Graham, Heather K Robertson, Ellen T McIntosh, Shanna Wallace, Juliane Albright, David L affil: School of Social Work, University of Alabama , Tuscaloosa, AL 35487-0314 , United States sug: subj: Machine Learning Alcohol Drinking Epidemiology Alcoholism Diagnosis Alcoholism Epidemiology Alcoholism Prevention and Control Human Cross Sectional Studies Alabama Middle Age Referral and Consultation Quantitative Studies Funding Source Sociodemographic Factors Descriptive Statistics Univariate Statistics Comparative Studies Middle Aged: 45-64 years ab: Introduction: This study utilizes a machine learning model to predict unhealthy alcohol use treatment levels among women of childbearing age. Methods: In this cross-sectional study, women of childbearing age (n = 2397) were screened for alcohol use over a 2-year period as part of the AL-SBIRT (screening, brief intervention, and referral to treatment in Alabama) program in three healthcare settings across Alabama for unhealthy alcohol use severity and depression. A support vector machine learning model was estimated to predict unhealthy alcohol use scores based on depression score and age. Results: The machine learning model was effective in predicting no intervention among patients with lower Patient Health Questionnaire (PHQ)-2 scores of any age, but a brief intervention among younger patients (aged 18–27 years) with PHQ-2 scores >3 and a referral to treatment for unhealthy alcohol use among older patients (between the ages of 25 and 50) with PHQ-2 scores >4. Conclusions: The machine learning model can be an effective tool in predicting unhealthy alcohol use treatment levels and approaches. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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