Research ArticleOriginal Research

A Machine Learning Approach to Identification of Unhealthy Drinking

Levi N. Bonnell, Benjamin Littenberg, Safwan R. Wshah and Gail L. Rose
The Journal of the American Board of Family Medicine May 2020, 33 (3) 397-406; DOI: https://doi.org/10.3122/jabfm.2020.03.190421

References

  1. 1.↵
    Substance Abuse and Mental Health Services Administration (SAMHSA). 2015 National Survey on Drug Use and Health (NSDUH). Table 2.46B—alcohol use, binge alcohol use, and heavy alcohol use in past month among persons aged 12 or older, by demographic characteristics: percentages, 2014 and 2015. Available from: https://www.samhsa.gov/data/sites/default/files/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015/NSDUH-DetTabs-2015.htm#tab2-46b. Published 2015. Accessed December 18, 2018.
  2. 2.↵
    National Institute on Alcohol Abuse and Alcoholism. Drinking levels defined. Available from: https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/moderate-binge-drinking. Published 2015. Accessed December 18, 2018.
  3. 3.↵
    1. Abdul-Rahman AK,
    2. Card TR,
    3. Grainge MJ,
    4. Fleming KM
    . All-cause and cause-specific mortality rates of patients treated for alcohol use disorders: a meta-analysis. Subst Abus 2018;39:509–517.
    OpenUrl
  4. 4.↵
    1. Laramee P,
    2. Leonard S,
    3. Buchanan-Hughes A,
    4. Warnakula S,
    5. Daeppen JB,
    6. Rehm J
    . Risk of all-cause mortality in alcohol-dependent individuals: a systematic literature review and meta-analysis. EBioMedicine 2015;2:1394–404.
    OpenUrl
  5. 5.↵
    1. Holman CD,
    2. English DR,
    3. Milne E,
    4. Winter MG
    . Meta-analysis of alcohol and all-cause mortality: a validation of NHMRC recommendations. Med J Aust 1996;164:141–5.
    OpenUrlPubMedWeb of Science
  6. 6.↵
    1. Connor J
    . Alcohol consumption as a cause of cancer. Addiction 2017;112:222–8.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Dawson DA,
    2. Grant BF,
    3. Stinson FS,
    4. Chou PS
    . Psychopathology associated with drinking and alcohol use disorders in the college and general adult populations. Drug Alcohol Depend 2005;77:139–50.
    OpenUrlCrossRefPubMedWeb of Science
  8. 8.↵
    1. Rehm J,
    2. Allamani A,
    3. Elekes Z,
    4. et al
    . Alcohol dependence and treatment utilization in Europe—a representative cross-sectional study in primary care. BMC Fam Pract 2015;16:90.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Rehm J,
    2. Room R,
    3. Graham K,
    4. Monteiro M,
    5. Gmel G,
    6. Sempos CT
    . The relationship of average volume of alcohol consumption and patterns of drinking to burden of disease: an overview. Addiction 2003;98:1209–28.
    OpenUrlCrossRefPubMedWeb of Science
  10. 10.↵
    1. Huang C,
    2. Zhan J,
    3. Liu YJ,
    4. Li DJ,
    5. Wang SQ,
    6. He QQ
    . Association between alcohol consumption and risk of cardiovascular disease and all-cause mortality in patients with hypertension: a meta-analysis of prospective cohort studies. Mayo Clin Proc 2014;89:1201–10.
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Sacks JJ,
    2. Gonzales KR,
    3. Bouchery EE,
    4. Tomedi LE,
    5. Brewer RD
    . 2010 national and state costs of excessive alcohol consumption. Am J Prev Med 2015;49:e73–e79.
    OpenUrlCrossRefPubMed
  12. 12.↵
    Centers for Disease Control and Prevention (CDC). Alcohol and public health: Alcohol-Related Disease Impact (ARDI). Average for United States 2006–2010 alcohol-attributable deaths due to excessive alcohol use. Available from: https://nccd.cdc.gov/DPH_ARDI/Default/Report.aspx?T=AAM&P=f6d7eda7-036e-4553-9968-9b17ffad620e&R=d7a9b303-48e9-4440-bf47-070a4827e1fd&M=8E1C5233-5640-4EE8-9247-1ECA7DA325B9&F=&D=. Accessed December 1, 2018.
  13. 13.↵
    1. Moyer VA
    ; Preventive Services Task Force. Screening and behavioral counseling interventions in primary care to reduce alcohol misuse: U.S. preventive services task force recommendation statement. Ann Intern Med 2013;159:210–8.
    OpenUrlPubMedWeb of Science
  14. 14.↵
    1. Bush K,
    2. Kivlahan DR,
    3. McDonell MB,
    4. Fihn SD,
    5. Bradley KA
    . The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Arch Intern Med 1998;158:1789–95.
    OpenUrlCrossRefPubMedWeb of Science
  15. 15.↵
    1. Bradley KA,
    2. DeBenedetti AF,
    3. Volk RJ,
    4. Williams EC,
    5. Frank D,
    6. Kivlahan DR
    . AUDIT-C as a brief screen for alcohol misuse in primary care. Alcoholism Clin Exp Res 2007;31:1208–17.
    OpenUrlCrossRefPubMedWeb of Science
  16. 16.↵
    1. Smith PC,
    2. Schmidt SM,
    3. Allensworth-Davies D,
    4. Saitz R
    . A single-question screening test for drug use in primary care. Arch Intern Med 2010;170:1155–60.
    OpenUrlCrossRefPubMedWeb of Science
  17. 17.↵
    1. Denny CH,
    2. Hungerford DW,
    3. McKnight-Eily LR,
    4. et al
    . Self-reported prevalence of alcohol screening among U.S. adults. Am J Prev Med 2016;50:380–3.
    OpenUrl
  18. 18.↵
    1. Johnson M,
    2. Jackson R,
    3. Guillaume L,
    4. Meier P,
    5. Goyder E
    . Barriers and facilitators to implementing screening and brief intervention for alcohol misuse: a systematic review of qualitative evidence. J Public Health (Oxf) 2011;33:412–21.
    OpenUrlCrossRefPubMedWeb of Science
  19. 19.↵
    1. Fortney J,
    2. Mukherjee S,
    3. Curran G,
    4. Fortney S,
    5. Han X,
    6. Booth BM
    . Factors associated with perceived stigma for alcohol use and treatment among at-risk drinkers. J Behav Health Serv Res 2004;31:418–29.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Beich A,
    2. Gannik D,
    3. Malterud K
    . Screening and brief intervention for excessive alcohol use: qualitative interview study of the experiences of general practitioners. BMJ 2002;325:870.
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    1. Onders R,
    2. Spillane J,
    3. Reilley B,
    4. Leston J
    . Use of electronic clinical reminders to increase preventive screenings in a primary care setting: blueprint from a successful process in Kodiak, Alaska. J Prim Care Community Health 2014;5:50–54.
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Williams EC,
    2. Achtmeyer CE,
    3. Thomas RM,
    4. et al
    . Factors underlying quality problems with alcohol screening prompted by a clinical reminder in primary care: a multi-site qualitative study. J Gen Intern Med 2015;30:1125–1132.
    OpenUrl
  23. 23.↵
    1. Hartz AJ,
    2. Guse C,
    3. Kajdacsy-Balla A
    . Identification of heavy drinkers using a combination of laboratory tests. J Clin Epidemiol 1997;50:1357–1368.
    OpenUrlCrossRefPubMedWeb of Science
  24. 24.↵
    1. Lichtenstein MJ,
    2. Burger MC,
    3. Yarned JWG,
    4. Elwood PC,
    5. Sweetnam PM
    . Derivation and validation of a prediction rule for identifying heavy consumers of alcohol. Alcoholism Clin Exp Res 1989;13:626–630.
    OpenUrlCrossRefPubMedWeb of Science
  25. 25.↵
    1. Harasymiw J,
    2. Seaberg J,
    3. Bean P
    . Detection of alcohol misuse using a routine test panel: the early detection of alcohol consumption (EDAC) test. Alcohol Alcohol 2004;39:329–335.
    OpenUrlCrossRefPubMedWeb of Science
  26. 26.↵
    1. Harasymiw JW,
    2. Vinson DC,
    3. Bean P
    . The early detection of alcohol consumption (EDAC) score in the identification of heavy and at-risk drinkers from routine blood tests. J Addict Dis 2000;19:43–59.
    OpenUrlPubMedWeb of Science
  27. 27.↵
    1. Korzec S,
    2. Korzec A,
    3. Conigrave K,
    4. Gisolf J,
    5. Tabakoff B
    . Validation of the Bayesian Alcoholism Test compared to single biomarkers in detecting harmful drinking. Alcohol Alcohol 2009;44:398–402.
    OpenUrlCrossRefPubMedWeb of Science
  28. 28.↵
    1. Easton JF,
    2. Stephens CR,
    3. Angelova M
    . Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality: a data mining approach. Comput Biol Med 2014;54:199–210.
    OpenUrl
  29. 29.↵
    1. Faisal M,
    2. Scally A,
    3. Howes R,
    4. Beatson K,
    5. Richardson D,
    6. Mohammed MA
    . A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation. Health Informatics J 2018;1460458218813600.
  30. 30.↵
    1. Ross EG,
    2. Shah NH,
    3. Dalman RL,
    4. Nead KT,
    5. Cooke JP,
    6. Leeper NJ
    . The use of machine learning for the identification of peripheral artery disease and future mortality risk. J Vasc Surg 2016;64:1515–1522, e1513.
    OpenUrlCrossRef
  31. 31.↵
    1. Horng S,
    2. Sontag DA,
    3. Halpern Y,
    4. Jernite Y,
    5. Shapiro NI,
    6. Nathanson LA
    . Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One 2017;12:e0174708.
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Arostegui I,
    2. Gonzalez N,
    3. Fernández-de-Larrea N,
    4. et al
    . Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer. Clin Epidemiol 2018;10:235–251.
    OpenUrl
  33. 33.↵
    1. D'Amico EJ,
    2. Paddock SM,
    3. Burnam A,
    4. Kung FY
    . Identification of and guidance for problem drinking by general medical providers: results from a national survey. Med Care 2005;43:229–236.
    OpenUrlCrossRefPubMedWeb of Science
  34. 34.↵
    1. De Oliveira E,
    2. Foster D,
    3. McGee Harper M,
    4. et al
    . Alcohol consumption raises HDL cholesterol levels by increasing the transport rate of apolipoproteins A-I and A-II. Circulation 2000;102:2347–2352.
    OpenUrlAbstract/FREE Full Text
  35. 35.↵
    1. Worrall S,
    2. Jersey J,
    3. Wilce PA,
    4. Seppä K,
    5. Hurme L,
    6. Sillanaukee P
    . Relationship between alcohol intake and immunoglobulin a immunoreactivity with acetaldehyde-modified bovine serum albumin. Alcoholism Clin Exp Res 1996;20:836–840.
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Steyerberg EW
    . Clinical prediction models: a practical approach to development, validation and updating. New York (NY): Springer; 2010.
  37. 37.↵
    1. Burges C
    . A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998;2:121–167.
    OpenUrl
  38. 38.↵
    1. Zhang GP
    . Neural networks for classification: a survey. IEEE Trans Syst Man Cy C 2000;30:451–462.
    OpenUrl
  39. 39.↵
    1. Altman NS
    . An Introduction to Kernel and nearest-neighbor nonparametric regression. Am Stat 1992;46:175–185.
    OpenUrlCrossRefWeb of Science
  40. 40.↵
    1. Quinlan JR
    . Induction of decision trees. Mach Learn 1986;1:81–106.
    OpenUrlCrossRef
  41. 41.↵
    1. Breiman L
    . Random forests. Mach Learn 2001;45:5–32.
    OpenUrlCrossRefPubMedWeb of Science
  42. 42.↵
    1. Friedman J,
    2. Tibshirani R,
    3. Hastie T
    . The elements of statistical learning. New York (NY): Springer New York Inc.; 2001.
  43. 43.↵
    1. Zhang Y,
    2. Xin Y,
    3. Li Q,
    4. et al
    . Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications. Biomed Eng Online 2017;16:125.
    OpenUrl
  44. 44.↵
    1. Hanley JA,
    2. McNeil BJ
    . The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29–36.
    OpenUrlCrossRefPubMedWeb of Science
  45. 45.↵
    1. Witten IH,
    2. Frank E,
    3. Hall MA
    . Data mining: practical machine learning tools and techniques. Burlington (MA): Morgan Kaufmann Publishers Inc.; 2011.
  46. 46.↵
    1. Korzec A,
    2. de Bruijn C,
    3. van Lambalgen M
    . The Bayesian Alcoholism Test had better diagnostic properties for confirming diagnosis of hazardous and harmful alcohol use. J Clin Epidemiol 2005;58:1024–1032.
    OpenUrlCrossRefPubMedWeb of Science
  47. 47.↵
    1. Gaudard M,
    2. Ramsey P,
    3. Stephens M
    . Interactive data mining and design of experiments: The JMP partition and custom design platforms. Cary, North Carolina: North Haven Group, LLC; 2006. http://islab.soe.uoguelph.ca/sareibi/PROJECTS_dr/GRAD_FUTURE_dr/docs/Interactive_DataMining.pdf.
  48. 48.↵
    1. Held E,
    2. Cape J,
    3. Tintle N
    . Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data. BMC Proc 2016;10:141–145.
    OpenUrl
  49. 49.↵
    1. Christodoulou E,
    2. Ma J,
    3. Collins GS,
    4. Steyerberg EW,
    5. Verbakel JY,
    6. van Calster B
    . A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019;110:12–22.
    OpenUrlCrossRefPubMed
  50. 50.↵
    1. Istvan J,
    2. Matarazzo JD
    . Tobacco, alcohol, and caffeine use: a review of their interrelationships. Psychol Bull 1984;95:301–326.
    OpenUrlCrossRefPubMedWeb of Science
  51. 51.↵
    1. Klinger EV,
    2. Carlini SV,
    3. Gonzalez I,
    4. et al
    . Accuracy of race, ethnicity, and language preference in an electronic health record. J Gen Intern Med 2015;30:719–723.
    OpenUrl
  52. 52.↵
    1. Davis CG,
    2. Thake J,
    3. Vilhena N
    . Social desirability biases in self-reported alcohol consumption and harms. Addict Behav 2010;35:302–311.
    OpenUrlCrossRefPubMedWeb of Science
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools

Jump to section

Keywords