Mostrar el registro sencillo del ítem
Machine learning aplicado a la predicción y clasificación de la depresión, un enfoque hacia la gestión de la salud mental
dc.contributor.advisor | Giraldo Marín, Liliana María | |
dc.contributor.advisor | Jaramillo Villegas, Herman Horacio | |
dc.contributor.author | Osorio Castrillón, Sebastián | |
dc.coverage.spatial | Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degreesLong: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees | |
dc.date | 2022-10-05 | |
dc.date.accessioned | 2022-11-15T19:20:38Z | |
dc.date.available | 2022-11-15T19:20:38Z | |
dc.identifier.other | T 0281 2022 | |
dc.identifier.uri | http://hdl.handle.net/11407/7637 | |
dc.description | El principal reto de las aseguradoras de la salud es pasar de una gestión reactiva a una gestión proactiva de sus afiliados, es decir, priorizar la prevención e implementación de acciones que favorezcan la salud en sus dimensiones tanto físicas como mentales y sociales, y no solo que mitiguen la enfermedad. Este artículo presenta una revisión sistemática de la literatura sobre las principales metodologías de aprendizaje automático que permiten a través de la predicción de enfermedades mentales, realizar una intervención temprana. Se encontró que las principales metodologías para dicho fin son los modelos estadísticos como la regresión logística y las redes neuronales; y que los diferentes indicadores de las neuroimágenes y el comportamiento en redes sociales se convierten en variables predictoras fundamentales a la hora de predecir las enfermedades mentales. | spa |
dc.description | The main challenge for health insurers is to move from reactive to proactive management of their clients, that is, prioritize prevention and implementation of actions that favor health in its physical, mental, and social dimensions, and not only that mitigate the disease. This article presents a systematic review of the literature on the main machine learning methodologies that allow, through the prediction of mental illnesses, to carry out an early intervention. It was found that the main methodologies for this purpose are statistical models such as logistic regression and neural networks; and that the different indicators of neuroimaging and behavior in social networks become fundamental predictor variables when it comes to predicting mental illnesses. | eng |
dc.format.extent | p. 1-69 | |
dc.format.medium | Electrónico | |
dc.format.mimetype | application/pdf | |
dc.language.iso | spa | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0 | * |
dc.subject | Aprendizaje automático | spa |
dc.subject | Minería de datos | spa |
dc.subject | Predicción enfermedades mentales | spa |
dc.subject | systematic review | eng |
dc.subject | Machine learning | eng |
dc.subject | Depression | eng |
dc.title | Machine learning aplicado a la predicción y clasificación de la depresión, un enfoque hacia la gestión de la salud mental | spa |
dc.type | info:eu-repo/semantics/masterThesis | |
dc.rights.accessrights | info:eurepo/semantics/openAccess | |
dc.publisher.program | Maestría en Modelación y Ciencia computacional | spa |
dc.subject.lemb | Aprendizaje automático (Inteligencia artificial) | spa |
dc.subject.lemb | Compañías aseguradoras | spa |
dc.subject.lemb | Depresión mental - Métodos estadísticos | spa |
dc.subject.lemb | Estadísticas médicas | spa |
dc.subject.lemb | Minería de datos | spa |
dc.subject.lemb | Predicción (Psicología) | spa |
dc.subject.lemb | Salud mental | spa |
dc.subject.lemb | Servicios de salud mental | spa |
dc.relation.citationstartpage | 1 | |
dc.relation.citationendpage | 69 | |
dc.audience | Comunidad Universidad de Medellín | spa |
dc.publisher.faculty | Facultad de Ciencias Básicas | spa |
dc.publisher.place | Medellín | spa] |
dc.type.hasversion | publishedVersion | |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
dc.relation.references | Aladag, A. E., Muderrisoglu, S., Akbas, N. B., Zahmacioglu, O., & Bingol, H. O. (2018). Detecting suicidal ideation on forums: Proof-of-concept study. Journal of Medical Internet Research, 20(6). https://doi.org/10.2196/jmir.9840 | spa |
dc.relation.references | Alberdi, A., Weakley, A., Schmitter-Edgecombe, M., Cook, D. J., Aztiria, A., Basarab, A., & Barrenechea, M. (2018). Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer’s Disease. IEEE Journal of Biomedical and Health Informatics, 22(6), 1720–1731. https://doi.org/10.1109/JBHI.2018.2798062 | spa |
dc.relation.references | Barros, J., Morales, S., Echávarri, O., García, A., Ortega, J., Asahi, T., Moya, C., Fischman, R., Maino, M. P., & Núñez, C. (2017). Suicide detection in Chile: Proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders. Revista Brasileira de Psiquiatria, 39(1), 1–11. https://doi.org/10.1590/1516-4446-2015-1877 | spa |
dc.relation.references | Beevers, C. G., Mullarkey, M. C., Dainer-Best, J., Stewart, R. A., Labrada, J., Allen, J. J. B., McGeary, J. E., & Shumake, J. (2019). Association between negative cognitive bias and depression: A symptom-level approach. Journal of Abnormal Psychology, 128(3), 212–227. https://doi.org/10.1037/abn0000405 | spa |
dc.relation.references | Castro, H. M. L. (2005). Revista de psiquiatria y salud mental hermilio valdizan estigma y enfermedad mental: un punto de vista historico-social. | spa |
dc.relation.references | Dinga, R., Marquand, A. F., Veltman, D. J., Beekman, A. T. F., Schoevers, R. A., van Hemert, A. M., Penninx, B. W. J. H., & Schmaal, L. (2018). Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Translational Psychiatry, 8(1). https://doi.org/10.1038/s41398-018-0289-1 | spa |
dc.relation.references | Galatzer-Levy, I. R., Ma, S., Statnikov, A., Yehuda, R., & Shalev, A. Y. (2017). Utilization of machine learning for prediction of post-traumatic stress: A re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Translational Psychiatry, 7(3). https://doi.org/10.1038/tp.2017.38 | spa |
dc.relation.references | Iniesta, R., Malki, K., Maier, W., Rietschel, M., Mors, O., Hauser, J., Henigsberg, N., Dernovsek, M. Z., Souery, D., Stahl, D., Dobson, R., Aitchison, K. J., Farmer, A., Lewis, C. M., McGuffin, P., & Uher, R. (2016). Combining clinical variables to optimize prediction of antidepressant treatment outcomes. Journal of Psychiatric Research, 78, 94–102. https://doi.org/10.1016/j.jpsychires.2016.03.016 | spa |
dc.relation.references | Interactivechao, https://interactivechaos.com/es/manual/tutorial-de-machine-learning/gradient-boosting | spa |
dc.relation.references | Jan, A., Meng, H., Gaus, Y. F. B. A., & Zhang, F. (2018). Artificial Intelligent System for Automatic Depression Level Analysis Through Visual and Vocal Expressions. IEEE Transactions on Cognitive and Developmental Systems, 10(3), 668–680. https://doi.org/10.1109/TCDS.2017.2721552 | spa |
dc.relation.references | Jin, H., Wu, S., & di Capua, P. (2015). Development of a clinical forecasting model to predict comorbid depression among diabetes patients and an application in depression screening policy making. Preventing Chronic Disease, 12(9). https://doi.org/10.5888/pcd12.150047 | spa |
dc.relation.references | Kessler, R. C., van Loo, H. M., Wardenaar, K. J., Bossarte, R. M., Brenner, L. A., Cai, T., Ebert, D. D., Hwang, I., Li, J., de Jonge, P., Nierenberg, A. A., Petukhova, M. v., Rosellini, A. J., Sampson, N. A., Schoevers, R. A., Wilcox, M. A., & Zaslavsky, A. M. (2016). Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Molecular Psychiatry, 21(10), 1366–1371. https://doi.org/10.1038/mp.2015.198 | spa |
dc.relation.references | Koutsouleris, N., Kambeitz-Ilankovic, L., Ruhrmann, S., Rosen, M., Ruef, A., Dwyer, D. B., Paolini, M., Chisholm, K., Kambeitz, J., Haidl, T., Schmidt, A., Gillam, J., Schultze-Lutter, F., Falkai, P., Reiser, M., Riecher-Rössler, A., Upthegrove, R., Hietala, J., Salokangas, R. K. R., … Borgwardt, S. (2018). Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or with Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry, 75(11), 1156–1172. https://doi.org/10.1001/jamapsychiatry.2018.2165 | spa |
dc.relation.references | Kumar, S., & Chong, I. (2018). Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states. International Journal of Environmental Research and Public Health, 15(12). https://doi.org/10.3390/ijerph15122907 | spa |
dc.relation.references | Le, T. T., Fu, W., & Moore, J. H. (2020). Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics, 36(1), 250–256. https://doi.org/10.1093/bioinformatics/btz470 | spa |
dc.relation.references | Oh, J., Yun, K., Hwang, J. H., & Chae, J. H. (2017). Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales. Frontiers in Psychiatry, 8. https://doi.org/10.3389/fpsyt.2017.00192 | spa |
dc.relation.references | O’Halloran, R., Kopell, B. H., Sprooten, E., Goodman, W. K., & Frangou, S. (2016). Multimodal neuroimaging-informed clinical applications in neuropsychiatric disorders. Frontiers in Psychiatry, 7(APR). https://doi.org/10.3389/fpsyt.2016.00063 | spa |
dc.relation.references | OMS. (2012). Plan de acciόn sobre salud mental 2013-2020. Organización Mundial de la Salud. https://www.who.int/mental_health/publications/action_plan/es | spa |
dc.relation.references | OMS. (2016). ¿Qué es la promoción de la salud? Organización Mundial de la Salud. https://www.who.int/features/qa/health-promotion/es/ | spa |
dc.relation.references | OMS. (2019). La OMS subraya la urgencia de transformar la salud mental y los cuidados conexos. https://www.who.int/es/news/item/17-06-2022-who-highlights-urgent-need-to-transform-mental-health-and-mental-health-care | spa |
dc.relation.references | OMS. (2020). INVERTIR EN SALUD MENTAL Día Mundial de la Salud Mental 2020 | spa |
dc.relation.references | OMS. (2021). Depresión. https://www.who.int/es/news-room/fact-sheets/detail/depression | spa |
dc.relation.references | Passos, I. C., Mwangi, B., Cao, B., Hamilton, J. E., Wu, M. J., Zhang, X. Y., Zunta-Soares, G. B., Quevedo, J., Kauer-Sant’Anna, M., Kapczinski, F., & Soares, J. C. (2016). Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach. Journal of Affective Disorders, 193, 109–116. https://doi.org/10.1016/j.jad.2015.12.066 | spa |
dc.relation.references | Patel, M. J., Andreescu, C., Price, J. C., Edelman, K. L., Reynolds, C. F., & Aizenstein, H. J. (2015). Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. International Journal of Geriatric Psychiatry, 30(10), 1056–1067. https://doi.org/10.1002/gps.4262 | spa |
dc.relation.references | Pérez Rave, J. (2019). Revisión Sistemática de Literatura en Ingeniería, ampliada y actualizada (Sello Editorial IDINNOV, Dic. 2019, ISBN: 978‐958‐58897‐6‐7). | spa |
dc.relation.references | Redlich, R., Opel, N., Grotegerd, D., Dohm, K., Zaremba, D., Burger, C., Munker, S., Muhlmann, L., Wahl, P., Heindel, W., Arolt, V., Alferink, J., Zwanzger, P., Zavorotnyy, M., Kugel, H., & Dannlowski, U. (2016). Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry, 73(6), 557–564. https://doi.org/10.1001/jamapsychiatry.2016.0316 | spa |
dc.relation.references | Reps, J. M., Schuemie, M. J., Suchard, M. A., Ryan, P. B., & Rijnbeek, P. R. (2018). Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. Journal of the American Medical Informatics Association, 25(8), 969–975. https://doi.org/10.1093/jamia/ocy032 | spa |
dc.relation.references | Ryu, S., Lee, H., Lee, D. K., & Park, K. (2018). Use of a machine learning algorithm to predict individuals with suicide ideation in the general population. Psychiatry Investigation, 15(11), 1030–1036. https://doi.org/10.30773/pi.2018.08.27 | spa |
dc.relation.references | Schnyer, D. M., Clasen, P. C., Gonzalez, C., & Beevers, C. G. (2017). Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Research - Neuroimaging, 264, 1–9. https://doi.org/10.1016/j.pscychresns.2017.03.003 | spa |
dc.relation.references | Tran, T., & Kavuluru, R. (2017). Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks. Journal of Biomedical Informatics, 75, S138–S148. https://doi.org/10.1016/j.jbi.2017.06.010 | spa |
dc.relation.references | Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016). Mobile sensing and support for people with depression: A pilot trial in the wild. JMIR MHealth and UHealth, 4(3). https://doi.org/10.2196/mhealth.5960 | spa |
dc.relation.references | Wu, M. J., Wu, H. E., Mwangi, B., Sanches, M., Selvaraj, S., Zunta-Soares, G. B., & Soares, J. C. (2015). Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: A pattern classification approach. Journal of Psychiatric Research, 62, 84–91. https://doi.org/10.1016/j.jpsychires.2015.01.015 | spa |
dc.rights.creativecommons | Attribution-NonCommercial-ShareAlike 4.0 International | * |
dc.type.local | Tesis de Maestría | spa |
dc.description.degreename | Magíster en Modelación y Ciencia computacional | spa |
dc.description.degreelevel | Maestría | spa |
dc.publisher.grantor | Universidad de Medellín | spa |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
Tesis [673]