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Application of Techniques Based on Artificial Intelligence for Predicting the Consumption of Drugs and Substances. A Systematic Mapping Review

  • Pablo Torres-CarriónEmail author
  • Ruth Reátegui
  • Priscila Valdiviezo
  • Byron Bustamante
  • Silvia Vaca
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

The consumption of alcohol, drugs and substances constitutes one of the most serious public health problems worldwide, in particular due to the social consequences related to violence, abandonment of studies, family disintegration and the great problem of drug trafficking that is strengthened at the increase consumption. In addition, Artificial Intelligence is consolidating as an area of ​​interdisciplinary knowledge, helping with the agile relationship of variables and indicators, which facilitates the discovery of behavioral patterns of human and material entities. From this perspective, the present investigation is proposed, which details universal indicators regarding the research carried out in the conjunction of these areas of knowledge. A systematic search has been carried out of scientific articles of journals indexed in the Scopus and WoS databases. There is an exhibition organized by subareas, countries, researchers, problems raised, regarding scientific production to facilitate possible future research in these areas.

Keywords

Consumption of drugs Artificial intelligence Systematic mapping review Prediction models 

References

  1. 1.
    United Nations Office on Drugs and Crime: World Drug report, Vienna, Austria (2019)Google Scholar
  2. 2.
    Herrera Rodríguez, A., et al.: Policonsumo simultáneo de drogas en estudiantes de facultades de ciencias de la salud/ciencias médicas en siete universidades de cinco países de América Latina y un país del Caribe: implicaciones de género, legales y sociales (2012)Google Scholar
  3. 3.
    United Nations Office on Drugs and Crime: World Drug Report, Vienna, Austria (2017)Google Scholar
  4. 4.
    Szolovits, P.: Artificial Intelligence in Medicine. Routledge, Abingdon (2019)CrossRefGoogle Scholar
  5. 5.
    Cohen, S., Kamarck, T., Mermelstein, R.: A global measure of perceived stress. J. Health Soc. Behav. 24(4), 385–396 (1983)CrossRefGoogle Scholar
  6. 6.
    Kroenke, K., Spitzer, R.L., Williams, J.B.W.: The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001)CrossRefGoogle Scholar
  7. 7.
    Hughes, M.E., Waite, L.J., Hawkley, L.C., Cacioppo, J.T.: A short scale for measuring loneliness in large surveys: results from two population-based studies. Res. Aging. 26, 655–672 (2004)CrossRefGoogle Scholar
  8. 8.
    Ruiz, F.J., Luciano, C., Cangas, A.J., Beltrán, I.: Measuring experiential avoidance and psychological inflexibility: the Spanish version of the acceptance and action questionnaire-II. Psicothema 25, 123–129 (2013)Google Scholar
  9. 9.
    Steinberg, L., Sharp, C., Stanford, M.S., Tharp, A.T.: New tricks for an old measure: the development of the barratt impulsiveness scale-brief (BIS-Brief). Psychol. Assess. 25, 216 (2013)CrossRefGoogle Scholar
  10. 10.
    Renau, V., Oberst, U., Gosling, S., Rusiñol, J., Chamarro, A.: Translation and validation of the ten-item-personality inventory into Spanish and Catalan. Aloma Rev. Psicol. Ciències l’Educació i l’Esport. 31, 85–97 (2013) Google Scholar
  11. 11.
    Diener, E.D., Emmons, R.A., Larsen, R.J., Griffin, S.: The satisfaction with life scale. J. Pers. Assess. 49, 71–75 (1985)CrossRefGoogle Scholar
  12. 12.
    Babor, T.F., Higgins-Biddle, J.C., Saunders, J.B., Monteiro, M.G.: Audit: The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Health Care. World Health Organization, Geneva (2001)Google Scholar
  13. 13.
    Saunders, J.B., Aasland, O.G., Babor, T.F., De la Fuente, J.R., Grant, M.: Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction 88, 791–804 (1993)CrossRefGoogle Scholar
  14. 14.
    Heatherton, T.F., Kozlowski, L.T., Frecker, R.C., Fagerstrom, K.: The Fagerström test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br. J. Addict. 86(9), 1119–1127 (1991)CrossRefGoogle Scholar
  15. 15.
    Ruisoto, P., Cacho, R., López-Goñi, J.J., Vaca, S., Jiménez, M.: Prevalence and profile of alcohol consumption among university students in Ecuador. Gac. Sanit. 30, 370–374 (2016)CrossRefGoogle Scholar
  16. 16.
    Iavindrasana, J., Cohen, G., Depeursinge, A., Müller, H., Meyer, R., Geissbuhler, A.: Clinical data mining: a review. Yearb. Med. Inform. 18, 121–133 (2009)CrossRefGoogle Scholar
  17. 17.
    Hamet, P., Tremblay, J.: Artif. Intell. Med. Metab. Exp. 69, S36–S40 (2017)Google Scholar
  18. 18.
    Kumar, R., Sharma, A., Haris Siddiqui, M., Kumar Tiwari, R.: Prediction of metabolism of drugs using artificial intelligence: how far have we reached? Curr. Drug Metab. 17, 129–141 (2016)CrossRefGoogle Scholar
  19. 19.
    Chakradhar, S.: Predictable response: finding optimal drugs and doses using artificial intelligence. Nat. Med. 23, 1244–1247 (2017)CrossRefGoogle Scholar
  20. 20.
    Jothi, N., Husain, W.: Data mining in healthcare–a review. Procedia Comput. Sci. 72, 306–313 (2015)CrossRefGoogle Scholar
  21. 21.
    Long, C.: Data Science and Big Data Analytics. Discovering, Analyzing, Visualizing and Presenting Data. Wiley, Indianapolis (2015)Google Scholar
  22. 22.
    Géron, A.: Hands-on Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Inc., Sebastopol (2017)Google Scholar
  23. 23.
    Torres-Carrión, P., González-González, C., Aciar, S., Rodríguez-Morales, G.: Methodology for systematic literature review applied to engineering and education. In: EDUCON2018 – IEEE Global Engineering Education Conference, Santa Cruz de Tenerife – España. IEEE Xplore Digital Library (2018)Google Scholar
  24. 24.
    Schulte, B., Kaner, E.F.S., Beyer, F., Schmidt, C.S., O’Donnell, A.: Study protocol for a systematic review of evidence for digital interventions for comorbid excessive drinking and depression in community-dwelling populations. BMJ Open 9(10), e031503 (2019)CrossRefGoogle Scholar
  25. 25.
    Mak, K.K., Lee, K., Park, C.: Applications of machine learning in addiction studies: a systematic review. Psychiatry Res. 275, 53–60 (2019)CrossRefGoogle Scholar
  26. 26.
    Canan, C., Polinski, J.M., Alexander, G.C., Kowal, M.K., Brennan, T.A., Shrank, W.H.: Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review. J. Am. Med. Inform. Assoc. 24, 1204–1210 (2017)CrossRefGoogle Scholar
  27. 27.
    García-González, A., Ramírez-Montoya, M.-S.: Systematic mapping of scientific production on open innovation (2015–2018): opportunities for sustainable training environments. Sustainability 11, 1–15 (2019)Google Scholar
  28. 28.
    Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., Gonzalez, G.: Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J. Am. Med. Inform. Assoc. 22, 671–681 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universidad Técnica Particular de LojaLojaEcuador

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