{"id":28232,"date":"2022-06-30T09:18:44","date_gmt":"2022-06-30T14:18:44","guid":{"rendered":"https:\/\/saluddigital.com\/?p=28232"},"modified":"2025-10-20T11:36:57","modified_gmt":"2025-10-20T17:36:57","slug":"cientificos-se-apoyan-en-ia-para-guiar-a-pacientes-a-mejorar-su-bienestar-emocional-con-el-paso-del-tiempo","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/cientificos-se-apoyan-en-ia-para-guiar-a-pacientes-a-mejorar-su-bienestar-emocional-con-el-paso-del-tiempo\/","title":{"rendered":"Scientists Lean on AI to Guide Patients to Improve Their Emotional Well-Being Over Time"},"content":{"rendered":"
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Researchers at Massachusetts General Hospital have used Artificial Intelligence (AI) to explore psychological well-being and provide tools to support long-term patients.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The Massachusetts General Hospital team, in collaboration with Deep Longevity Limited, developed AI models to explore psychological well-being and create tools to help people improve long-term life satisfaction, especially in old age.<\/p>

The results of the research were published in the journal aging <\/em>in an article entitled: \u201cOptimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability\u201d. The research presented a model of deep learning and human psychology, which is capable of predicting the user's current age and future well-being. Likewise, through a 2D map, the study presents a diagram of the brain regions that are most vulnerable to depression.<\/p>

"In this paper, we highlight aging-related trends in well-being and have presented a dynamic model of human psychology that allows one to maximize the individual level of happiness," explained co-author and Dr. Nancy Etcoff.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The article studied several variables related to long-term psychological well-being: positive relationships, self-acceptance, autonomy, personal growth, environmental mastery, purpose in life, years, depressive affect, depressed affect, and anhedonia. The collection of this data is based on the application of questionnaires and deep learning.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Through the development of models based on the mentioned variables, the authors explain that it is possible that these data help to develop online tools for the improvement of mental health, interventions guided by therapists and even mobile applications.<\/p>

In addition to the variables mentioned, the study was able to identify another 32 variables used to make the individual predictions of the patients, in which satisfaction with current life, closeness to the community, interpersonal relationships, daily activities, among others, stand out.<\/p>

\u201cTo address the first part of our research goal, we preselected 32 psychological variables associated with well-being and created a predictor of age and future well-being. The age predictor, as expected, showed poor performance as the features on which it was built were not chosen to be representative of age,\u201d the authors explain.<\/p>

Furthermore, they developed a learning model based on SOM to provide personalized self-improvement goals. "SOMs are a type of unsupervised learning model that share technological similarities with neural networks." SOMs are used to describe people's mental health and determine mental disorders based on transcribed speech from patients. \u201cThe cited model has reached a high precision (97%) when predicting the type of mental illness\u201d, explains the study.<\/p>

Learn more about this study at the following link: https:\/\/www.aging-us.com\/article\/204061\/text<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t<\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t BIBLIOGRAPHY<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t

AGING<\/strong><\/p>

https:\/\/www.aging-us.com\/article\/204061\/text<\/a><\/p>

GENERAL MASS<\/strong><\/p>

https:\/\/www.massgeneral.org\/news\/press-release\/artificial-intelligence-may-guide-individuals-to-improved-well-being<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Investigadores del Massachusetts General Hospital, han utilizado Inteligencia Artificial (IA), para explorar el bienestar psicol\u00f3gico y brindar herramientas que apoyen a los pacientes a largo plazo. El equipo del Massachusetts General Hospital en colaboraci\u00f3n con Deep Longevity Limited, desarrollaron modelos de IA para explorar el bienestar psicol\u00f3gico y crear herramientas que ayuden a las personas a mejorar la satisfacci\u00f3n con la vida a largo plazo, especialmente durante la vejez. Los resultados de la investigaci\u00f3n fueron publicados en la revista Aging en un art\u00edculo titulado: \u201cOptimizando el bienestar futuro con inteligencia artificial: mapas autoorganizados (SOMs) para la identificaci\u00f3n de islas de estabilidad emocional\u201d. La investigaci\u00f3n present\u00f3 un modelo de aprendizaje profundo y de psicolog\u00eda humana, que es capaz de predecir la edad actual del usuario y su bienestar futuro. Asimismo, a trav\u00e9s de un mapa 2D el estudio presenta un esquema de las regiones del cerebro que son m\u00e1s vulnerables a la depresi\u00f3n. \u201cEn este trabajo, destacamos las tendencias relacionadas con el envejecimiento en el bienestar y hemos presentado un modelo din\u00e1mico de psicolog\u00eda humana que permite maximizar el nivel individual de felicidad\u201d, explic\u00f3 la coautora y Dra. Nancy Etcoff. El art\u00edculo estudi\u00f3 diversas variables relacionadas con el bienestar psicol\u00f3gico a largo plazo: Relaciones positivas, autoaceptaci\u00f3n, autonom\u00eda, crecimiento personal, dominio ambiental, prop\u00f3sito en la vida, a\u00f1os, afecto depresivo, afecto deprimido y anhedonia. La recopilaci\u00f3n de estos datos se basan la aplicaci\u00f3n de cuestionarios y aprendizaje profundo. A trav\u00e9s del desarrollo de modelos basado en las variables mencionadas, los autores explican que es posible que esos datos ayuden a desarrollar herramientas en l\u00ednea para la mejora de la salud mental, intervenciones guiadas por terapeutas e incluso aplicaciones m\u00f3viles. Adem\u00e1s de las variables mencionadas, el estudio pudo identificar otras 32 variables utilizadas para realizar las predicciones individuales de los pacientes, en las que destacan, la satisfacci\u00f3n con la vida actual, cercan\u00eda con la comunidad, relaciones interpersonales, actividades diarias, entre otras. \u201cPara abordar la primera parte de nuestro objetivo de investigaci\u00f3n, preseleccionamos 32 variables psicol\u00f3gicas asociadas con el bienestar y creamos un predictor de edad y bienestar futuro. El predictor de edad, como era de esperar, mostr\u00f3 un rendimiento deficiente ya que las caracter\u00edsticas sobre las que se construy\u00f3 no se eligieron para ser representativas de la edad\u201d, explican los autores. Por otra parte desarrollaron un modelo de aprendizaje basado en SOM para proporcionar objetivos personalizados de superaci\u00f3n personal. \u201cLos SOM son un tipo de modelo de aprendizaje no supervisado que comparte similitudes tecnol\u00f3gicas con las redes neuronales\u201d. Los SOM son utilizados para describir la salud mental de las personas y determinar trastornos mentales en funci\u00f3n del habla transcrita de los pacientes. \u201cEl modelo citado ha alcanzado una alta precisi\u00f3n (97%) al predecir el tipo de enfermedad mental\u201d, explica el estudio. Conoce m\u00e1s sobre este estudio en el siguiente enlace: https:\/\/www.aging-us.com\/article\/204061\/text BIBLIOGRAF\u00cdA AGING https:\/\/www.aging-us.com\/article\/204061\/text MASS GENERAL https:\/\/www.massgeneral.org\/news\/press-release\/artificial-intelligence-may-guide-individuals-to-improved-well-being<\/p>","protected":false},"author":1,"featured_media":28234,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,160],"tags":[145],"class_list":["post-28232","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/28232","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=28232"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/28232\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/28234"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=28232"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=28232"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=28232"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}