Comparación de técnicas de aprendizaje automático para la clasificación de pacientes con trastornos mentales y de comportamiento debido al consumo de psicotrópicos en la ciudad de Barranquilla
Publicado
:Número
:Sección
:Licencia
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Cómo citar
Dimensions Metrics
aprendizaje automático, máquinas de soporte vectorial, bosque aleatorio, red neuronal artificial, sustancia psicoactiva
Antecedentes: El trastorno por consumo de sustancia psicoactivas contribuye a una carga sustancial mundial de enfermedad, a pesar de los continuos esfuerzos de las entidades gubernamentales para mitigar esta problemática. Este problema es uno de los campos de investigación actuales más atractivo para desarrollar modelos de aprendizaje automático. Objetivo: Este proyecto de investigación tuvo como objetivo comparar cuatro modelos de aprendizaje automático para la clasificación de pacientes con trastornos mentales y de comportamiento debido al consumo de psicotrópicos ubicados en la clase de intoxicación agudo o síndrome de dependencia en la ciudad de Barranquilla. Método: El método utilizado consistió en entrenar, validar y comparar cuatro técnicas de aprendizaje automático con bases de datos de pacientes de Barranquilla. Resultados: Los resultados revelaron que bosque aleatorio y regresión logística arrojaron la mejor precisión (72%). No obstante, red neuronal artificial es el mejor modelo para predecir la proporción de casos verdaderamente positivos entre los casos positivos detectados. Por otra parte, el mejor clasificador que predice la proporción de casos positivos que están bien detectadas es bosque aleatorio, asimismo el mejor clasificador que proporciona la más alta de casos negativo que están bien detectados es máquina de soporte vectorial. Finalmente, cabe mencionar que red neuronal artificial y bosque aleatorio son los clasificadores que mejor área bajo la curva registran con 80% cada uno. Conclusiones: En términos generales, red neuronal artificial y bosque aleatorio mostraron indicios de ser un buen clasificador para discriminar entre pacientes que potencialmente estaría en un caso de intoxicación aguda o síndrome de dependencia, obteniendo valores promedios de desempeño entre 80 y 90%.
B.F. Grant et al., “Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders - Results from the national epidemiologic survey on alcohol and related conditions,” Arch. Gen. Psychiatry, vol. 61, no. 8, pp. 807–816, 2004
R.C. Kessler, C.B. Nelson, K.A. McGonagle, M. J. Edlund, R. G. Frank, and P. J. Leaf, “The epidemiology of co-occurring addictive and mental disorders: Implications for prevention and service utilization,” Am. J. Orthopsychiatry, vol. 66, no. 1, pp. 17–31, 1996
D. A. Regier et al., “Comorbidity of Mental Disorders With Alcohol and Other Drug Abuse: Results From the Epidemiologic Catchment Area (ECA) Study,” JAMA, vol. 264, no. 19, pp. 2511–2518, 1990
WHO, “Mental Health Action Plan 2013-2020.,” Ginebra, Suiza, 2013
L.M. Squeglia et al., “Neural Predictors of Initiating Alcohol Use During Adolescence,” Am. J. Psychiatry, vol. 174, no. 2, pp. 172–185, Feb. 2017
T. Katsuki, T.K. Mackey, and R. Cuomo, “Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data,” J. Med. INTERNET Res., vol. 17, no. 12, 2015
B. F. Grant, D. A. Dawson, F. S. Stinson, P. S. Chou, W. Kay, and R. Pickering, “The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in a general population sample,” Drug Alcohol Depend., vol. 71, no. 1, pp. 7–16, Jul. 2003
R. Ali et al., “The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility,” ADDICTION, vol. 97, no. 9, pp. 1183–1194, Sep. 2002
J.E. Schulenberg and J. L. Maggs, “A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood,” J. Stud. Alcohol, no. 14, pp. 54–70, Mar. 2002
H. W. Perkins, “Social norms and the prevention of alcohol misuse in collegiate contexts,” J. Stud. Alcohol, no. 14, pp. 164–172, Mar. 2002
M. Dennis et al., “The Cannabis Youth Treatment (CYT) Study: Main findings from two randomized trials,” J. Subst. Abuse Treat., vol. 27, no. 3, pp. 197–213, Oct. 2004
R.W. Hingson, T. Heeren, R.C. Zakocs, A. Kopstein, and H. Wechsler, “Magnitude of alcohol-related mortality and morbidity among US college students ages 18-24,” J. Stud. Alcohol, vol. 63, no. 2, pp. 136–144, Mar. 2002
J.R. Greenmyer, M.G. Klug, C. Kambeitz, S. Popova, and L. Burd, “A Multicountry Updated Assessment of the Economic Impact of Fetal Alcohol Spectrum Disorder: Costs for Children and Adults,” J. Addict. Med., vol. 12, no. 6, pp. 466–473, 2018
C. Potier, V. Laprevote, F. Dubois-Arber, O. Cottencin, and B. Rolland, “Supervised injection services: What has been demonstrated? A systematic literature review,” Drug Alcohol Depend., vol. 145, pp. 48–68, 2014
L. Lu, Y. Fang, and X. Wang, “Drug abuse in China: Past, present and future,” Cell. Mol. Neurobiol., vol. 28, no. 4, pp. 479–490, Jun. 2008
R.B. Felson and J. Staff, “Committing Economic Crime for Drug Money,” CRIME Delinq., vol. 63, no. 4, pp. 375–390, 2017
S. Metternich, S. Zoerntlein, T. Schoenberger, and C. Huhn, “Ion mobility spectrometry as a fast screening tool for synthetic cannabinoids to uncover drug trafficking in jail via herbal mixtures, paper, food, and cosmetics,” DRUG Test. Anal., vol. 11, no. 6, pp. 833–846, Jun. 2019
D.S. Dolliver, S. P. Ericson, and K. L. Love, “A Geographic Analysis of Drug Trafficking Patterns on the TOR Network,” Geogr. Rev., vol. 108, no. 1, pp. 45–68, 2018
E.-U. Nelson and I. Obot, “Beyond prohibition: responses to illicit drugs in West Africa in an evolving policy context,” DRUGS AND ALCOHOL TODAY, 2020
S. Gharaei-Manesh, A. Fathzadeh, and R. Taghizadeh-Mehrjardi, “Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran,” Cold Reg. Sci. Technol., vol. 122, pp. 26–35, Feb. 2016
Y.S. Kim, “Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size,” Expert Syst. Appl., vol. 34, no. 2, pp. 1227–1234, Feb. 2008
E. Reza, R. Arash, and M. Behrouz, “Comparison of Classification Methods Based on the Type of Attributes and Sample Size,” J. Converg. Inf. Technol., vol. 4, no. 3, pp. 94–102, Sep. 2009
D.A. Salazar, J.I. Velez, and J.C. Salazar, “Comparison between SVM and Logistic Regression: Which One is Better to Discriminate?,” Rev. Colomb. Estadística, vol. 35, no. 2, pp. 223–237, 2012
J. M. Moguerza and A. Muñoz, “Support Vector Machines with Applications,” Stat. Sci., vol. 21, no. 3, pp. 322–336, 2006
Wasserman, “All of Statistics : A Concise Course in Statistical Inference Brief Contents,” Simulation, vol. C, p. 461, 2004
L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001
AFCEA International Press, Darpa Neural Network Study. 1988
S. Haykin, Neural networks: A comprehensive foundation. Pearson, 1994
J. Manuel Gutiérrez, “Introducción a las Redes Neuronales,” España, 2016
Antecedentes: El trastorno por consumo de sustancia psicoactivas contribuye a una carga sustancial mundial de enfermedad, a pesar de los continuos esfuerzos de las entidades gubernamentales para mitigar esta problemática. Este problema es uno de los campos de investigación actuales más atractivo para desarrollar modelos de aprendizaje automático. Objetivo: Este proyecto de investigación tuvo como objetivo comparar cuatro modelos de aprendizaje automático para la clasificación de pacientes con trastornos mentales y de comportamiento debido al consumo de psicotrópicos ubicados en la clase de intoxicación agudo o síndrome de dependencia en la ciudad de Barranquilla. Método: El método utilizado consistió en entrenar, validar y comparar cuatro técnicas de aprendizaje automático con bases de datos de pacientes de Barranquilla. Resultados: Los resultados revelaron que bosque aleatorio y regresión logística arrojaron la mejor precisión (72%). No obstante, red neuronal artificial es el mejor modelo para predecir la proporción de casos verdaderamente positivos entre los casos positivos detectados. Por otra parte, el mejor clasificador que predice la proporción de casos positivos que están bien detectadas es bosque aleatorio, asimismo el mejor clasificador que proporciona la más alta de casos negativo que están bien detectados es máquina de soporte vectorial. Finalmente, cabe mencionar que red neuronal artificial y bosque aleatorio son los clasificadores que mejor área bajo la curva registran con 80% cada uno. Conclusiones: En términos generales, red neuronal artificial y bosque aleatorio mostraron indicios de ser un buen clasificador para discriminar entre pacientes que potencialmente estaría en un caso de intoxicación aguda o síndrome de dependencia, obteniendo valores promedios de desempeño entre 80 y 90%.
B.F. Grant et al., “Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders - Results from the national epidemiologic survey on alcohol and related conditions,” Arch. Gen. Psychiatry, vol. 61, no. 8, pp. 807–816, 2004
R.C. Kessler, C.B. Nelson, K.A. McGonagle, M. J. Edlund, R. G. Frank, and P. J. Leaf, “The epidemiology of co-occurring addictive and mental disorders: Implications for prevention and service utilization,” Am. J. Orthopsychiatry, vol. 66, no. 1, pp. 17–31, 1996
D. A. Regier et al., “Comorbidity of Mental Disorders With Alcohol and Other Drug Abuse: Results From the Epidemiologic Catchment Area (ECA) Study,” JAMA, vol. 264, no. 19, pp. 2511–2518, 1990
WHO, “Mental Health Action Plan 2013-2020.,” Ginebra, Suiza, 2013
L.M. Squeglia et al., “Neural Predictors of Initiating Alcohol Use During Adolescence,” Am. J. Psychiatry, vol. 174, no. 2, pp. 172–185, Feb. 2017
T. Katsuki, T.K. Mackey, and R. Cuomo, “Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data,” J. Med. INTERNET Res., vol. 17, no. 12, 2015
B. F. Grant, D. A. Dawson, F. S. Stinson, P. S. Chou, W. Kay, and R. Pickering, “The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in a general population sample,” Drug Alcohol Depend., vol. 71, no. 1, pp. 7–16, Jul. 2003
R. Ali et al., “The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility,” ADDICTION, vol. 97, no. 9, pp. 1183–1194, Sep. 2002
J.E. Schulenberg and J. L. Maggs, “A developmental perspective on alcohol use and heavy drinking during adolescence and the transition to young adulthood,” J. Stud. Alcohol, no. 14, pp. 54–70, Mar. 2002
H. W. Perkins, “Social norms and the prevention of alcohol misuse in collegiate contexts,” J. Stud. Alcohol, no. 14, pp. 164–172, Mar. 2002
M. Dennis et al., “The Cannabis Youth Treatment (CYT) Study: Main findings from two randomized trials,” J. Subst. Abuse Treat., vol. 27, no. 3, pp. 197–213, Oct. 2004
R.W. Hingson, T. Heeren, R.C. Zakocs, A. Kopstein, and H. Wechsler, “Magnitude of alcohol-related mortality and morbidity among US college students ages 18-24,” J. Stud. Alcohol, vol. 63, no. 2, pp. 136–144, Mar. 2002
J.R. Greenmyer, M.G. Klug, C. Kambeitz, S. Popova, and L. Burd, “A Multicountry Updated Assessment of the Economic Impact of Fetal Alcohol Spectrum Disorder: Costs for Children and Adults,” J. Addict. Med., vol. 12, no. 6, pp. 466–473, 2018
C. Potier, V. Laprevote, F. Dubois-Arber, O. Cottencin, and B. Rolland, “Supervised injection services: What has been demonstrated? A systematic literature review,” Drug Alcohol Depend., vol. 145, pp. 48–68, 2014
L. Lu, Y. Fang, and X. Wang, “Drug abuse in China: Past, present and future,” Cell. Mol. Neurobiol., vol. 28, no. 4, pp. 479–490, Jun. 2008
R.B. Felson and J. Staff, “Committing Economic Crime for Drug Money,” CRIME Delinq., vol. 63, no. 4, pp. 375–390, 2017
S. Metternich, S. Zoerntlein, T. Schoenberger, and C. Huhn, “Ion mobility spectrometry as a fast screening tool for synthetic cannabinoids to uncover drug trafficking in jail via herbal mixtures, paper, food, and cosmetics,” DRUG Test. Anal., vol. 11, no. 6, pp. 833–846, Jun. 2019
D.S. Dolliver, S. P. Ericson, and K. L. Love, “A Geographic Analysis of Drug Trafficking Patterns on the TOR Network,” Geogr. Rev., vol. 108, no. 1, pp. 45–68, 2018
E.-U. Nelson and I. Obot, “Beyond prohibition: responses to illicit drugs in West Africa in an evolving policy context,” DRUGS AND ALCOHOL TODAY, 2020
S. Gharaei-Manesh, A. Fathzadeh, and R. Taghizadeh-Mehrjardi, “Comparison of artificial neural network and decision tree models in estimating spatial distribution of snow depth in a semi-arid region of Iran,” Cold Reg. Sci. Technol., vol. 122, pp. 26–35, Feb. 2016
Y.S. Kim, “Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size,” Expert Syst. Appl., vol. 34, no. 2, pp. 1227–1234, Feb. 2008
E. Reza, R. Arash, and M. Behrouz, “Comparison of Classification Methods Based on the Type of Attributes and Sample Size,” J. Converg. Inf. Technol., vol. 4, no. 3, pp. 94–102, Sep. 2009
D.A. Salazar, J.I. Velez, and J.C. Salazar, “Comparison between SVM and Logistic Regression: Which One is Better to Discriminate?,” Rev. Colomb. Estadística, vol. 35, no. 2, pp. 223–237, 2012
J. M. Moguerza and A. Muñoz, “Support Vector Machines with Applications,” Stat. Sci., vol. 21, no. 3, pp. 322–336, 2006
Wasserman, “All of Statistics : A Concise Course in Statistical Inference Brief Contents,” Simulation, vol. C, p. 461, 2004
L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001
AFCEA International Press, Darpa Neural Network Study. 1988
S. Haykin, Neural networks: A comprehensive foundation. Pearson, 1994
J. Manuel Gutiérrez, “Introducción a las Redes Neuronales,” España, 2016