AI Article Synopsis
- The study uses a machine learning model to predict the treatment levels for unhealthy alcohol use in women of childbearing age.
- The research involved screening 2397 women over two years through the AL-SBIRT program in Alabama to gather data on alcohol use severity and depression.
- The results indicated that the model effectively predicted that younger women might need brief interventions, while older women were more likely to require referrals for treatment based on their depression scores.
Article Abstract
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.
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| http://dx.doi.org/10.1093/alcalc/agad075 | DOI Listing |
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