Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
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Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
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Sumario:

Esta revisión tiene como objetivo analizar el uso del procesamiento de lenguaje natural en las investigaciones de trastornos mentales en adultos, como la depresión, ansiedad y los sentimientos de duelo. Realizando una búsqueda en cuatro bases de datos relevantes (PubMed, IEEE, ScienceDirect y LILACS) publicado en español e inglés desde 2017 hasta 2022 sin restricciones de país de origen. Se utilizaron términos MeSH y de texto libre para identificar estudios sobre la implementación del procesamiento del leguaje natural en la detección de condiciones de salud mental como la ansiedad, depresión y sentimientos de duelo. Se encontraron un total de 136 estudios relacionados, de los cuales se seleccionaron 32 artículos para la revisión. Donde se m... Ver más

Guardado en:

Revista Iberoamericana de Psicología

2027-1786

2500-6517

Montoya Arenas, David Andrés

Torres Silva, Ever Augusto

Pereira Montiel, Eider

Alemán Acuña, Reyk Sayk

CORPORACIÓN UNIVERSITARIA IBEROAMERICANA

10.33881/2027-1786.rip.17202

17

2024-10-22

11

22

Colombia

processamento de linguagem natural

transtornos mentais

luto

depressão

ansiedade

saúde mental

trastornos mentales

duelo

depresión

ansiedad

salud mental

procesamiento de lenguaje natural

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Revista iberoamericana de psicología - 2023

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spelling Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
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processamento de linguagem natural
Artículo de revista
Núm. 2 , Año 2024 : Revista Iberoamericana de Psicología (Vol. 17 # 2)
2
17
transtornos mentais
luto
depressão
ansiedade
saúde mental
trastornos mentales
Bogotá: Corporación Universitaria Iberoamericana
duelo
depresión
ansiedad
salud mental
procesamiento de lenguaje natural
Montoya Arenas, David Andrés
Torres Silva, Ever Augusto
Pereira Montiel, Eider
Alemán Acuña, Reyk Sayk
Esta revisión tiene como objetivo analizar el uso del procesamiento de lenguaje natural en las investigaciones de trastornos mentales en adultos, como la depresión, ansiedad y los sentimientos de duelo. Realizando una búsqueda en cuatro bases de datos relevantes (PubMed, IEEE, ScienceDirect y LILACS) publicado en español e inglés desde 2017 hasta 2022 sin restricciones de país de origen. Se utilizaron términos MeSH y de texto libre para identificar estudios sobre la implementación del procesamiento del leguaje natural en la detección de condiciones de salud mental como la ansiedad, depresión y sentimientos de duelo. Se encontraron un total de 136 estudios relacionados, de los cuales se seleccionaron 32 artículos para la revisión. Donde se muestra un incremento de la utilización del procesamiento de lenguaje natural en la salud pública, espacialmente entre los años 2020 y 2022. Además, se observó que las redes sociales son una fuente de datos frecuentemente utilizada en estos estudios, y que los modelos de aprendizaje automático supervisados son los más prevalentes en la detección de depresión y ansiedad. El procesamiento de lenguaje natural puede mejorar la detección de problemas de salud mental en la salud pública. Los métodos de aprendizaje supervisados supervisado son los más comunes, pero los algoritmos basados en aprendizaje profundo presentan perspectivas innovadoras y se espera que esta tecnología siga en aumento para mejorar la detección y tratamiento de trastornos mentales. Es importante continuar investigando y desarrollando estas tecnologías para su aplicada en la salud pública.  
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Revista Iberoamericana de Psicología
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018). Automated EEG-based screening of depression using deep convolutional neural network. Computer Methods and Programs in Biomedicine, 161, 103–113. https://doi.org/10.1016/j.cmpb.2018.04.012
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Chiong, R., Budhi, G. S., Dhakal, S., & Chiong, F. (2021). A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Computers in Biology and Medicine, 135. https://doi.org/10.1016/j.compbiomed.2021.104499
Charles Roe. (2017, December 6). Identify Data Patterns with Natural Language Processing and Machine Learning. DATAVERSITY. https://www.dataversity.net/identify-data-patterns-natural-language-processing-machine-learning/#
Chae, S. W., & Lee, S. H. (2022). Sharing emotion while spectating video game play: Exploring Twitch users’ emotional change after the outbreak of the COVID-19 pandemic. Computers in Human Behavior, 131. https://doi.org/10.1016/j.chb.2022.107211
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Burkhardt, H. A., Alexopoulos, G. S., Pullmann, M. D., Hull, T. D., Areán, P. A., & Cohen, T. (2021). Behavioral activation and depression symptomatology: Longitudinal assessment of linguistic indicators in text-based therapy sessions. Journal of Medical Internet Research, 23(7). https://doi.org/10.2196/28244
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Boivin, J., O., M., Duong, M., Cooper, O., Filipenko, D., Markert, M., Samuelsen, C., & Lenderking, W. R. (2022). Emotional reactions to infertility diagnosis: thematic and natural language processing analyses of the 1000 Dreams survey. Reproductive BioMedicine Online. https://doi.org/10.1016/j.rbmo.2022.08.107
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https://reviberopsicologia.ibero.edu.co/article/view/2731
anxiety
This review aims to analyze the use of natural language processing in research on mental disorders in adults, such as depression, anxiety, and grief. A search was conducted in four relevant databases (PubMed, IEEE, ScienceDirect, and LILACS) for articles published in Spanish and English from 2017 to 2022 without restrictions on country of origin. MeSH terms and free-text terms were used to identify studies on the implementation of natural language processing in the detection of mental health conditions such as anxiety, depression, and grief. A total of 136 related studies were found, of which 32 articles were selected for review. The findings show an increase in the utilization of natural language processing in public health, especially between 2020 and 2022. Furthermore, it was observed that social media is a frequently used data source in these studies, and supervised machine learning models are the most prevalent in detecting depression and anxiety. Natural language processing can improve the detection of mental health problems in public health. Supervised learning methods are the most common, but deep learning algorithms present innovative perspectives, and it is expected that this technology will continue to grow to enhance the detection and treatment of mental disorders. It is important to continue researching and developing these technologies for their application in public health.  
Use of Natural Language Processing in Mental Health: Scoping Review
natural language processing
mental health
depression
grief
mental disorders
Journal article
11
https://reviberopsicologia.ibero.edu.co/article/download/2731/2047
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2027-1786
2024-10-22T00:00:00Z
2024-10-22T00:00:00Z
2024-10-22
22
2500-6517
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collection Revista Iberoamericana de Psicología
title Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
spellingShingle Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
Montoya Arenas, David Andrés
Torres Silva, Ever Augusto
Pereira Montiel, Eider
Alemán Acuña, Reyk Sayk
processamento de linguagem natural
transtornos mentais
luto
depressão
ansiedade
saúde mental
trastornos mentales
duelo
depresión
ansiedad
salud mental
procesamiento de lenguaje natural
anxiety
natural language processing
mental health
depression
grief
mental disorders
title_short Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
title_full Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
title_fullStr Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
title_full_unstemmed Procesamiento de lenguaje natural en la Salud Mental: Revisión de alcance
title_sort procesamiento de lenguaje natural en la salud mental: revisión de alcance
title_eng Use of Natural Language Processing in Mental Health: Scoping Review
description Esta revisión tiene como objetivo analizar el uso del procesamiento de lenguaje natural en las investigaciones de trastornos mentales en adultos, como la depresión, ansiedad y los sentimientos de duelo. Realizando una búsqueda en cuatro bases de datos relevantes (PubMed, IEEE, ScienceDirect y LILACS) publicado en español e inglés desde 2017 hasta 2022 sin restricciones de país de origen. Se utilizaron términos MeSH y de texto libre para identificar estudios sobre la implementación del procesamiento del leguaje natural en la detección de condiciones de salud mental como la ansiedad, depresión y sentimientos de duelo. Se encontraron un total de 136 estudios relacionados, de los cuales se seleccionaron 32 artículos para la revisión. Donde se muestra un incremento de la utilización del procesamiento de lenguaje natural en la salud pública, espacialmente entre los años 2020 y 2022. Además, se observó que las redes sociales son una fuente de datos frecuentemente utilizada en estos estudios, y que los modelos de aprendizaje automático supervisados son los más prevalentes en la detección de depresión y ansiedad. El procesamiento de lenguaje natural puede mejorar la detección de problemas de salud mental en la salud pública. Los métodos de aprendizaje supervisados supervisado son los más comunes, pero los algoritmos basados en aprendizaje profundo presentan perspectivas innovadoras y se espera que esta tecnología siga en aumento para mejorar la detección y tratamiento de trastornos mentales. Es importante continuar investigando y desarrollando estas tecnologías para su aplicada en la salud pública.  
description_eng This review aims to analyze the use of natural language processing in research on mental disorders in adults, such as depression, anxiety, and grief. A search was conducted in four relevant databases (PubMed, IEEE, ScienceDirect, and LILACS) for articles published in Spanish and English from 2017 to 2022 without restrictions on country of origin. MeSH terms and free-text terms were used to identify studies on the implementation of natural language processing in the detection of mental health conditions such as anxiety, depression, and grief. A total of 136 related studies were found, of which 32 articles were selected for review. The findings show an increase in the utilization of natural language processing in public health, especially between 2020 and 2022. Furthermore, it was observed that social media is a frequently used data source in these studies, and supervised machine learning models are the most prevalent in detecting depression and anxiety. Natural language processing can improve the detection of mental health problems in public health. Supervised learning methods are the most common, but deep learning algorithms present innovative perspectives, and it is expected that this technology will continue to grow to enhance the detection and treatment of mental disorders. It is important to continue researching and developing these technologies for their application in public health.  
author Montoya Arenas, David Andrés
Torres Silva, Ever Augusto
Pereira Montiel, Eider
Alemán Acuña, Reyk Sayk
author_facet Montoya Arenas, David Andrés
Torres Silva, Ever Augusto
Pereira Montiel, Eider
Alemán Acuña, Reyk Sayk
topicspa_str_mv processamento de linguagem natural
transtornos mentais
luto
depressão
ansiedade
saúde mental
trastornos mentales
duelo
depresión
ansiedad
salud mental
procesamiento de lenguaje natural
topic processamento de linguagem natural
transtornos mentais
luto
depressão
ansiedade
saúde mental
trastornos mentales
duelo
depresión
ansiedad
salud mental
procesamiento de lenguaje natural
anxiety
natural language processing
mental health
depression
grief
mental disorders
topic_facet processamento de linguagem natural
transtornos mentais
luto
depressão
ansiedade
saúde mental
trastornos mentales
duelo
depresión
ansiedad
salud mental
procesamiento de lenguaje natural
anxiety
natural language processing
mental health
depression
grief
mental disorders
citationvolume 17
citationissue 2
citationedition Núm. 2 , Año 2024 : Revista Iberoamericana de Psicología (Vol. 17 # 2)
publisher Bogotá: Corporación Universitaria Iberoamericana
ispartofjournal Revista Iberoamericana de Psicología
source https://reviberopsicologia.ibero.edu.co/article/view/2731
language Español
format Article
rights http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Revista iberoamericana de psicología - 2023
https://creativecommons.org/licenses/by-nc-sa/4.0
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