Karen A Johnson, Justin T McDaniel, Joana Okine, Heather K Graham, Ellen T Robertson, Shanna McIntosh, Juliane Wallace, David L Albright
{"title":"预测阿拉巴马州育龄妇女不健康饮酒的机器学习模型。","authors":"Karen A Johnson, Justin T McDaniel, Joana Okine, Heather K Graham, Ellen T Robertson, Shanna McIntosh, Juliane Wallace, David L Albright","doi":"10.1093/alcalc/agad075","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>This study utilizes a machine learning model to predict unhealthy alcohol use treatment levels among women of childbearing age.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The machine learning model can be an effective tool in predicting unhealthy alcohol use treatment levels and approaches.</p>","PeriodicalId":7407,"journal":{"name":"Alcohol and alcoholism","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A machine learning model for the prediction of unhealthy alcohol use among women of childbearing age in Alabama.\",\"authors\":\"Karen A Johnson, Justin T McDaniel, Joana Okine, Heather K Graham, Ellen T Robertson, Shanna McIntosh, Juliane Wallace, David L Albright\",\"doi\":\"10.1093/alcalc/agad075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>This study utilizes a machine learning model to predict unhealthy alcohol use treatment levels among women of childbearing age.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The machine learning model can be an effective tool in predicting unhealthy alcohol use treatment levels and approaches.</p>\",\"PeriodicalId\":7407,\"journal\":{\"name\":\"Alcohol and alcoholism\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alcohol and alcoholism\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/alcalc/agad075\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SUBSTANCE ABUSE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alcohol and alcoholism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/alcalc/agad075","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SUBSTANCE ABUSE","Score":null,"Total":0}
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 (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.
期刊介绍:
About the Journal
Alcohol and Alcoholism publishes papers on the biomedical, psychological, and sociological aspects of alcoholism and alcohol research, provided that they make a new and significant contribution to knowledge in the field.
Papers include new results obtained experimentally, descriptions of new experimental (including clinical) methods of importance to the field of alcohol research and treatment, or new interpretations of existing results.
Theoretical contributions are considered equally with papers dealing with experimental work provided that such theoretical contributions are not of a largely speculative or philosophical nature.