Detección temprana del deterioro emocional mediante análisis de diarios personales usando inteligencia artificial y PLN

Cárdenas Rodríguez, Yodid Jair; Motta Méndez, José Fernando; Moreno Bedoya, David Leonardo

Publicación:
Detección temprana del deterioro emocional mediante análisis de diarios personales usando inteligencia artificial y PLN

dc.contributor.author	Cárdenas Rodríguez, Yodid Jair
dc.contributor.author	Motta Méndez, José Fernando
dc.contributor.author	Moreno Bedoya, David Leonardo
dc.creator.id	80872182
dc.creator.id	1000271981
dc.creator.id	79891338
dc.date.accessioned	2026-02-24T00:58:51Z
dc.date.issued	2026-02-02
dc.description.abstract	El monitoreo tradicional de la salud mental presenta limitaciones como la intervención tardía y barreras de acceso. Los diarios digitales constituyen una fuente rica de información emocional que aún no ha sido ampliamente explorada desde una perspectiva tecnológica. Este estudio propone diseñar una herramienta basada en procesamiento de lenguaje natural para analizar automáticamente el contenido emocional de diarios personales digitales, con el objetivo de facilitar la detección temprana de posibles signos de deterioro emocional. La propuesta combina técnicas de análisis computacional del lenguaje y métodos de inteligencia artificial aplicados al ámbito de la salud mental.	spa
dc.description.abstract	Traditional mental health monitoring has limitations such as late intervention and barriers to access. Digital diaries constitute a rich source of emotional information that has not yet been widely explored from a technological perspective. This study proposes designing a natural language processing-based tool to automatically analyze the emotional content of digital personal diaries, with the aim of facilitating the early detection of potential signs of emotional deterioration. The proposal combines computational language analysis techniques and artificial intelligence methods applied to the field of mental health.	eng
dc.description.degreelevel	Especialización
dc.description.degreename	Especialista en Machine Learning
dc.format	pdf
dc.format.extent	35 páginas, 1 anexo
dc.format.medium	Recurso electrónico	spa
dc.format.mimetype	application/pdf
dc.identifier.instname	instname:Universidad Ean	spa
dc.identifier.local	BDM-PML
dc.identifier.reponame	reponame:Repositorio Institucional Biblioteca Digital Minerva	spa
dc.identifier.repourl	repourl:https://repository.ean.edu.co/
dc.identifier.uri	https://hdl.handle.net/10882/19022
dc.language.iso	spa
dc.publisher	Univerisdad Ean
dc.publisher.faculty	Facultad de Ingeniería
dc.publisher.program	Especialización en Machine Learning
dc.relation.references	Kim, Y., Oh, Y., Oh, J., & Lee, U. (2023). MindfulDiary: Harnessing large language model to support psychiatric patients’ journaling. arXiv. https://arxiv.org/abs/2310.05231 Kim, S., Kim, Y., & Lee, U. (2024). Using large language models to detect depression from user-generated diary text data as a novel approach in digital mental health screening: Algorithm development and validation study. Journal of Medical Internet Research, 26, e54617. https://doi.org/10.2196/54617 Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology in behavioral healthcare. Professional Psychology: Research and Practice, 42(6), 505-512. https://doi.org/10.1037/a0024485 OMS. (2022). Mental health. https://www.who.int/news-room/fact-sheets/detail/mental-healthstrengthening-our-response Organización Mundial de la Salud (OMS) World Health Organization. (2022). World mental health report: Transforming mental health for all. https://www.who.int/publications/i/item/9789240049338 Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., ... & UnÜtzer, J. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392(10157), 1553-1598. https://doi.org/10.1016/S0140-6736(18)31612-X Clement, S., Schauman, O., Graham, T., Maggioni, F., Evans-Lacko, S., Bezborodovs, N., ... & Thornicroft, G. (2015). What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies. Psychological Medicine, 45(1), 11-27. https://doi.org/10.1017/S0033291714000129 Mojtabai, R., & Olfson, M. (2008). National trends in psychotherapy by office-based psychiatrists.Archives of General Psychiatry, 65(8), 962–970. https://doi.org/10.1001/archpsyc.65.8.962 Walker, E. R., Cummings, J. R., Hockenberry, J. M., & Druss, B. G. (2015). Insurance status, useof mental health services, and unmet need for mental health care in the United States. Psychiatric Services, 66 (6), 578–584. https://doi.org/10.1176/appi.ps.201400248 Bærøe, K., Miyata-Sturm, A., & Henden, E. (2020). Artificial intelligence and clinical decisionmaking: the ethical terrain. BMJ Health & Care Informatics, 27(3), e100113. https://doi.org/10.1136/bmjhci-2019-100113 De Angel, V., Chisholm, K., Freeman, D., & Lister, R. (2022). Digital monitoring of mood andmental health: a systematic review. The Lancet Psychiatry, 9(5), 360–372 Pennebaker, J. W., & Smyth, J. M. (2016). Opening Up by Writing It Down: How ExpressiveWriting Improves Health and Eases Emotional Pain (3rd ed.). Guilford Press. Morris, C. G., & Maisto, A. A. (2021). Introducción a la psicología (15.ª ed.). Pearson Educación. (Fragmento adaptado del capítulo sobre aplicación de la psicología en el sigloXXI, con mención a APA, 2018; Jones, 2020; Kerr, 2020). Torous, J., & Roberts, L. W. (2020). Needed innovation in digital health and smartphone applications for mental health: Transparency and trust. JAMA Psychiatry, 77(5), 441–442. Mozilla Foundation. (2022). Privacy Not Included: Mental Health Apps. Recuperado de https://foundation.mozilla.org/en/privacynotincluded/ Onnela, J.-P. (2021). Opportunities and challenges in the digital phenotyping of mental health. Nature Human Behaviour, 5, 1224–1236. Schoonenboom, J., & Johnson, R. B. (2017). How to construct a mixed methods research design. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 69, 107-131. Aldiabat, K. M., & Le Navenec, C. (2018). Data saturation: The mysterious step in grounded theory method. The Qualitative Report, 23(1), 245-261. https://doi.org/10.46743/2160-3715/2018.2994 Saunders, M. N. K., Lewis, P., & Thornhill, A. (2019). Research methods for business students(8th ed.). Pearson. Brooke, J. (1996). SUS: A quick and dirty usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & I. L. McClelland (Eds.), Usability evaluation in industry (pp. 189–194). Taylor & Francis Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713-3744. https://doi.org/10.1007/s11042-022-13428-4 Creswell, J. W., & Creswell, J. D. (2022). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications. Tashakkori, A., Johnson, R. B., & Teddlie, C. (2021). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences (3rd ed.). SAGE Publications. Wongkoblap, A., Vadillo, M. A., & Curcin, V. (2021). Researching mental health disorders in the era of social media: Systematic review. Journal of Medical Internet Research, 23(3), e22874. https://doi.org/10.2196/22874 Losada, D. E., & Crestani, F. (2016). A test collection for research on depression and language use. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality (CLPsych 2016) (pp. 1–10). Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-0301 Gratch, J., Lucas, G. M., King, A., & Morency, L.-P. (2014). The Distress Analysis Interview Corpus of human and computer interviews. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14). http://dcapswoz.ict.usc.edu Rowe, K., Patel, R., Broadbent, M., & Stewart, R. (2020). Natural language processing in mental health: A systematic review. NPJ Digital Medicine, 3, 54. https://doi.org/10.1038/s41746-020- 0254-1 Benton, A., Mitchell, M., & Hovy, D. (2021). Ethical challenges in data-driven mental health research. In Proceedings of the 2021 Conference on Fairness, Accountability, and Transparency (FAccT ’21) (pp. 191–200). ACM. https://doi.org/10.1145/3442188.3445910 Rowe, K., Jones, R., & Owens, C. (2020). The role of language in mental health diagnosis and treatment: A systematic review. Journal of Affective Disorders, 276, 680–692. https://doi.org/10.1016/j.jad.2020.07.119 Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 Rogers, A., Kovaleva, O., & Rumshisky, A. (2020). A primer in BERTology: What we know about how BERT works. Transactions of the Association for Computational Linguistics, 8, 842–866. https://doi.org/10.1162/tacl_a_00349 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: An integrative review. Current Opinion in Behavioral Sciences, 18, 43–49. https://doi.org/10.1016/j.cobeha.2017.07.005 Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics,17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003 Bergold, J., & Thomas, S. (2022). Participatory research methods: A methodological approach inmotion. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 13(1).https://doi.org/10.17169/fqs-13.1.1801 Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012 Ahmed, S. H., Kim, D., & Huh, E. N. (2022). Privacy-preserving techniques for healthcare data in IoT and cloud environments: A survey. Journal of King Saud University - Computer and Information Sciences, 34(1), 1202–1215. https://doi.org/10.1016/j.jksuci.2018.09.014 Ben-Zeev, D., Brian, R. M., Aschbrenner, K. A., Jonathan, G., & Mueser, K. T. (2015). Mobile technologies among people with serious mental illness: Opportunities for future services. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 555– 563.https://doi.org/10.1007/s10488-014-0597-8 Ullrich, P. M., & Lutgendorf, S. K. (2002).Journaling about stressful events: Effects of cognitive processing and emotional expression.Annals of Behavioral Medicine, 24(3), 244–250. https://doi.org/10.1207/S15324796ABM2403_10 Calvo, R. A., Milne, D. N., & Hussain, M. S. (2017).Natural language processing in mental health applications using non-clinical texts.Natural Language Engineering, 23(5), 649–685. https://doi.org/10.1017/S1351324916000383 Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019).Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/ Kahn, J. H., Tobin, R. M., Massey, A. E., & Anderson, J. A. (2007). Measuring emotional expression with the Linguistic Inquiry and Word Count.American Journal of Psychology, 120(2), 263–286. https://doi.org/10.2307/20445398 Inkster, B., Sarda, S., & Subramanian, V. (2018). Machine learning and mental health: Opportunities, challenges, and ethical implications.British Medical Bulletin, 129(1), 31–47. https://doi.org/10.1093/bmb/ldy026 Gruebner, O., Rapp, M. A., Adli, M., Kluge, U., Galea, S., & Heinz, A. (2017). Cities and mental health. Deutsches Ärzteblatt International, 114(8), 121–127. https://doi.org/10.3238/arztebl.2017.0121
dc.rights.accessrights	info:eu-repo/semantics/openAccess
dc.rights.coar	http://purl.org/coar/access_right/c_abf2
dc.rights.creativecommons	Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.license	Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.local	Abierto (Texto Completo)	spa
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.armarc	Inteligencia artificial	spa
dc.subject.armarc	Procesamiento de lenguaje natural (Computadores)	spa
dc.subject.armarc	Aprendizaje automático	spa
dc.subject.armarc	Salud mental	spa
dc.subject.armarc	Emociones	spa
dc.subject.proposal	Salud mental
dc.subject.proposal	Procesamiento de lenguaje natural
dc.subject.proposal	Inteligencia artificial
dc.subject.proposal	Análisis emocional
dc.subject.proposal	Detección temprana
dc.subject.proposal	Tecnología sanitaria
dc.subject.proposal	Mental health
dc.subject.proposal	Natural language processing
dc.subject.proposal	Artificial intelligence
dc.subject.proposal	Emotional analysis
dc.subject.proposal	Early detection
dc.subject.proposal	Healthcare technology
dc.title	Detección temprana del deterioro emocional mediante análisis de diarios personales usando inteligencia artificial y PLN	spa
dc.title	Early detection of emotional deterioration through analysis of personal diaries using artificial intelligence and NLP	eng
dc.type	Trabajo de grado - Especialización
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dc.type.other	Trabajo de grado - Especialización
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dspace.entity.type	Publication
person.affiliation.name	Especialización en Machine Learning
person.affiliation.name	Especialización en Machine Learning
person.affiliation.name	Especialización en Machine Learning