{"id":15610,"date":"2021-06-08T09:49:54","date_gmt":"2021-06-08T14:49:54","guid":{"rendered":"https:\/\/saluddigital.com\/?p=15610"},"modified":"2025-10-21T12:38:27","modified_gmt":"2025-10-21T18:38:27","slug":"modelo-de-aprendizaje-automatico-apoya-en-la-prediccion-de-resultados-de-pruebas-de-laboratorio","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/modelo-de-aprendizaje-automatico-apoya-en-la-prediccion-de-resultados-de-pruebas-de-laboratorio\/","title":{"rendered":"Machine learning model helps in the prediction of laboratory test results"},"content":{"rendered":"
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The machine learning model, was trained through data from wearable devices such as smart watches or wearables. It was designed to achieve predictions from laboratory tests. <\/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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Study published in Nature entitled \"Wearable sensors enable personalized predictions of clinical laboratory measurements\". A study that explains the importance of measuring and collecting vital sign data, such as heart rate and body temperature. In this way it is possible to monitor and detect clinical conditions.\u00a0<\/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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\u201cHere we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models,\u201d the study explains.<\/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 of the study showed that vital signs data obtained through wearables<\/em>provide a more accurate description of resting heart rate than measurements taken in a clinic. In addition, the data collected can be used to predict various vital sign measurements that were obtained in a clinic.<\/p>

\u201cThe length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models,\u201d the study explains.<\/p>

The results showed that commercial, portable smart devices like wearables, can indeed be used for continuous, longitudinal assessment of physiological measures that could normally only be measured with laboratory tests.<\/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

HEALTH IT ANALYTICS<\/strong><\/p>

https:\/\/healthitanalytics.com\/news\/machine-learning-model-helps-predict-clinical-lab-test-results<\/a>

<\/p>

NATURE<\/strong><\/p>

https:\/\/www.nature.com\/articles\/s41591-021-01339-0<\/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":"

El modelo de aprendizaje autom\u00e1tico, fue entrenado a trav\u00e9s de datos de dispositivos port\u00e1tiles como relojes inteligentes o wearables. Fue dise\u00f1ado para lograr predicciones de pruebas de laboratorio. Estudio publicado en Nature titulado “Sensores port\u00e1tiles permiten predicciones personalizadas de las mediciones de laboratorio cl\u00ednico\u201d. Es un estudio que explica la importancia de la medici\u00f3n y recolecci\u00f3n datos de signos vitales, como la frecuencia cardiaca y la temperatura corporal. De esta forma es posible controlar y detectar afecciones cl\u00ednicas.  \u201cExaminamos si los signos vitales medidos por los dispositivos port\u00e1tiles de los consumidores (es decir, la frecuencia card\u00edaca, la temperatura corporal, la actividad electrot\u00e9rmica y el movimiento controlados continuamente) pueden predecir los resultados de las pruebas de laboratorio cl\u00ednico utilizando modelos de aprendizaje autom\u00e1tico\u201d, explica el estudio. Los resultados del estudio mostraron que los datos de signos vitales a trav\u00e9s de wearables, brindan una descripci\u00f3n m\u00e1s precisa de la frecuencia cardiaca en reposo que las mediciones que se realizan en una cl\u00ednica. Adem\u00e1s, los datos recopilados, pueden ser utilizados para predecir diversas mediciones de signos vitales que se obtuvieron en cl\u00ednica. \u201cEl per\u00edodo de tiempo durante el cual se monitorean los signos vitales y la proximidad del per\u00edodo de monitoreo a la fecha de predicci\u00f3n juegan un papel fundamental en el rendimiento de los modelos de aprendizaje autom\u00e1tico\u201d, explica el estudio. Los resultados mostraron que los dispositivos inteligentes port\u00e1tiles comerciales, pueden utilizarse realmente para la evaluaci\u00f3n continua y longitudinal de medidas fisiol\u00f3gicas que normalmente solo se podr\u00edan medir con pruebas de laboratorio. BIBLIOGRAF\u00cdA HEALTH IT ANALYTICS https:\/\/healthitanalytics.com\/news\/machine-learning-model-helps-predict-clinical-lab-test-results NATURE https:\/\/www.nature.com\/articles\/s41591-021-01339-0<\/p>","protected":false},"author":1,"featured_media":15611,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,3395,156,1418],"tags":[145],"class_list":["post-15610","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-analitica","category-apps-moviles-e-internet-de-las-cosas","category-big-data","category-resena-de-publicaciones-cientificas","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/15610","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=15610"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/15610\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/15611"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=15610"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=15610"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=15610"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}