{"id":21547,"date":"2021-10-04T07:39:00","date_gmt":"2021-10-04T12:39:00","guid":{"rendered":"https:\/\/saluddigital.com\/?p=21547"},"modified":"2025-10-21T11:57:58","modified_gmt":"2025-10-21T17:57:58","slug":"investigacion-muestra-la-eficacia-de-la-inteligencia-artificial-para-predecir-malestar-psicologico","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/investigacion-muestra-la-eficacia-de-la-inteligencia-artificial-para-predecir-malestar-psicologico\/","title":{"rendered":"Research shows the effectiveness of Artificial Intelligence to predict psychological distress"},"content":{"rendered":"
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BMJ Open published a cross-sectional study comparing the use of Artificial Intelligence (AI) and psychiatric studies to measure psychological distress among workers.<\/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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Psychological discomfort is a problem that, as explained by the authors of the study carried out in Tsukuba Science City, in Japan, must be addressed in the field of occupational health. Their study aimed to use AI to predict psychological distress among workers. In this, sociodemographic, lifestyle and sleep factors were taken into account, not subjective information such as mood and emotions. The results of the psychiatrists were compared with the AI models.<\/p>

The study analyzed data from 7,251 workers. The AI model based on a neural network, calculated the precision, sensitivity and specificity. Furthermore, the six participating psychiatrists used the same data as the AI model for the prediction of psychological distress.<\/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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"An artificial intelligence model was created to predict psychological distress and then the results were compared in terms of accuracy with predictions made by psychiatrists," the authors explain.<\/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 results for the IA model accuracies in predicting moderate psychological distress were 65.2% and the psychiatrists' accuracies were 64.4%, so there were no significant differences. However, the accuracy of the AI model to predict severe psychological distress was 89.9%, against 85.5% for psychiatrists, so the AI in this case had a significantly higher accuracy.<\/p>

\u201cA machine learning model was successfully developed to assess workers with depressed mood. The explanatory variables used for the predictions did not ask directly about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views," the researchers concluded.<\/p>

Consult the investigation in the following link: https:\/\/bmjopen.bmj.com\/content\/11\/6\/e046265<\/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

BMJ-OPEN<\/strong><\/p>

https:\/\/bmjopen.bmj.com\/content\/11\/6\/e046265<\/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":"

BMJ Open public\u00f3 un estudio transversal compar\u00f3 el uso de Inteligencia Artificial (IA) y estudios psiqui\u00e1tricos para medir la angustia psicol\u00f3gica entre trabajadores. El malestar psicol\u00f3gico es un problema que como explican los autores del estudio realizado en Tsukuba Science City, en Jap\u00f3n, debe abordarse en el campo de la salud ocupacional. Su estudio tuvo como objetivo utilizar IA para predecir el malestar psicol\u00f3gico entre trabajadores. En este se tomaron en cuenta factores sociodemogr\u00e1ficos, de estilo de vida y del sue\u00f1o, no informaci\u00f3n subjetiva como el estado de \u00e1nimo y las emociones. Los resultados de los psiquiatras fueron comparados con los modelos de IA. El estudio analiz\u00f3 datos de 7 mil 251 trabajadores. El modelo de IA basado en una red neuronal, calcul\u00f3 la precisi\u00f3n, sensibilidad y especificidad. Adem\u00e1s, los seis psiquiatras participantes utilizaron los mismos datos que el modelo de IA para la predicci\u00f3n de angustia psicol\u00f3gica. \u201cSe cre\u00f3 un modelo de inteligencia artificial para predecir la angustia psicol\u00f3gica y luego se compararon los resultados en t\u00e9rminos de precisi\u00f3n con las predicciones realizadas por los psiquiatras\u201d, explican los autores. Los resultados de las precisiones del modelo de IA en la predicci\u00f3n de angustia psicol\u00f3gica moderada fueron de 65.2% y las precisiones de los psiquiatras fueron de 64.4%, por lo que no hubo diferencias significativas. No obstante, las precisiones del modelo de IA para predecir la angustia psicol\u00f3gica grave fueron de 89.9%, contra 85.5% de los psiquiatras por lo que la IA en este caso cont\u00f3 con una precisi\u00f3n significativamente mayor. \u201cSe desarroll\u00f3 con \u00e9xito un modelo de aprendizaje autom\u00e1tico para evaluar a los trabajadores con estado de \u00e1nimo deprimido. Las variables explicativas utilizadas para las predicciones no preguntaron directamente sobre el estado de \u00e1nimo. Por lo tanto, este modelo recientemente desarrollado parece ser capaz de predecir la angustia psicol\u00f3gica entre los trabajadores f\u00e1cilmente, independientemente de sus puntos de vista subjetivos\u201d, concluyeron los investigadores. Consulta la investigaci\u00f3n en el siguiente enlace: https:\/\/bmjopen.bmj.com\/content\/11\/6\/e046265 BIBLIOGRAF\u00cdA BMJ OPEN https:\/\/bmjopen.bmj.com\/content\/11\/6\/e046265<\/p>","protected":false},"author":1,"featured_media":21549,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-21547","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-big-data","category-inteligencia-artificial-y-ciencia","category-noticias","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/21547","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=21547"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/21547\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/21549"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=21547"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=21547"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=21547"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}