A machine learning model for the prediction of unhealthy alcohol use among women of childbearing age in Alabama.
Alcohol Alcohol
; 59(2)2024 Jan 17.
Article
en En
| MEDLINE
| ID: mdl-37968937
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Alcoholismo
Límite:
Adolescent
/
Adult
/
Female
/
Humans
/
Middle aged
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Alcohol Alcohol
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos