{"id":24292,"date":"2022-03-07T10:47:53","date_gmt":"2022-03-07T16:47:53","guid":{"rendered":"https:\/\/saluddigital.com\/?p=24292"},"modified":"2025-10-21T10:14:52","modified_gmt":"2025-10-21T16:14:52","slug":"investigacion-muestra-prediccion-sobre-riesgo-de-suicidio-a-traves-de-expedientes-clinicos-electronicos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/investigacion-muestra-prediccion-sobre-riesgo-de-suicidio-a-traves-de-expedientes-clinicos-electronicos\/","title":{"rendered":"Research Shows Suicide Risk Prediction Through Electronic Medical Records"},"content":{"rendered":"
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

The study aims to understand, predict and prevent suicidal behavior risk through structured and unstructured data in electronic medical records (EHR) and specialist notes, respectively.<\/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
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Researchers from psychiatry departments at Boston institutions such as Harvard University, Boston Children's Hospital, and Massachusetts General Hospital conducted a study on predicting suicide risk through structured and unstructured patient data.<\/p>

The study<\/a> published in npj Digital Medicine journal<\/em> shows the development of a clinical risk prediction model, developed through structured information from ECE and unstructured information such as doctors' notes, which was interpreted through natural language processing (NLP).<\/p>

In this sense, structured information includes information such as diagnoses and medication, so it is important to add unstructured data such as medical notes, so that the predictive model understands the value of each data classification and the interactions between the two.<\/p>

The study titled: Structured-Unstructured Predictive Interactions in EHR Models: A Case Study of Suicide Prediction<\/em>, contemplated three objectives:<\/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

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\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
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
  1. To compare the predictive value of structured and unstructured ECE data as independent data sets for predicting suicide risk.<\/li>
  2. Evaluate the increase in prediction performance when integrating structured and unstructured data using several models: Naive Bayes Classifier (NBC) and Random Forest (RF).<\/li>
  3. Identify pairs of structured and unstructured characteristics in which the interaction between the two characteristics differs substantially between populations with suicide attempts and people without attempts.<\/li><\/ol>\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
    \n\t\t\t\t\t\t
    \n\t\t\t\t\t
    \n\t\t\t
    \n\t\t\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t

    The application of the inclusion and exclusion criteria produced 1 million 625 thousand 350 training subjects for the 99% models, corresponding to non-cases and 16 thousand to cases, that is, the 1%.<\/p>

    The most commonly found captured variables in the dataset were impulse control disorder, bipolar disorder, schizoaffective disorder, and opioid dependence or abuse.<\/p>

    In this way it was possible to identify structured and unstructured data variables on patients at risk of suicide or suicidal behaviour. Based on these data, it is possible to develop effective suicide prevention strategies.<\/p>

    Check the full study at the following link:<\/p>

    https:\/\/www.nature.com\/articles\/s41746-022-00558-0<\/a><\/p>

    \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

    \n\t\t\t\t\t\t
    \n\t\t\t\t\t
    \n\t\t\t
    \n\t\t\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t\t\t
    \n\t\t\t\t\t\t\t
    \n\t\t\t\t\t
    \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

    NATURE<\/strong><\/p>

    https:\/\/www.nature.com\/articles\/s41746-022-00558-0<\/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 estudio tiene como objetivo entender, predecir y prevenir el riesgo conductas suicidas a trav\u00e9s de datos estructurados y no estructurados en expedientes cl\u00ednicos electr\u00f3nicos (ECE) y notas de los especialistas, respectivamente. Investigadores de departamentos de psiquiatr\u00eda de instituciones de Boston como la Universidad de Harvard, Boston Children\u2019s Hospital y Massachusetts General Hospital, realizaron un estudio sobre la predicci\u00f3n de riesgo de suicidio a trav\u00e9s de datos estructurados y no estructurados de pacientes. El estudio publicado en npj Digital Medicine muestra el desarrollo de un modelo cl\u00ednico de predicci\u00f3n de riesgo, desarrollado a trav\u00e9s de informaci\u00f3n estructurada de ECE e informaci\u00f3n no estructurada como las notas de los m\u00e9dicos, que fue interpretada a trav\u00e9s de procesamiento de lenguaje natural (PLN). En este sentido, la informaci\u00f3n estructurada incluye informaci\u00f3n como diagn\u00f3sticos y medicaci\u00f3n, por lo que es importante a\u00f1adir datos no estructurados como las notas m\u00e9dicas, para que el modelo predictivo entienda el valor de cada clasificaci\u00f3n de datos y las interacciones entre ambas. El estudio titulado: Interacciones predictivas estructuradas-no estructuradas en modelos EHR: un estudio de caso de predicci\u00f3n de suicidio, contempl\u00f3 tres objetivos: Comparar el valor predictivo de los datos de ECE estructurados y no estructurados como conjuntos de datos independientes para predecir el riesgo de suicidio. Evaluar el aumento en el rendimiento de la predicci\u00f3n al integrar datos estructurados y no estructurados utilizando varios modelos: Naive Bayes Classifier (NBC) y Random Forest (RF). Identificar pares de caracter\u00edsticas estructuradas y no estructuradas en las que la interacci\u00f3n entre las dos caracter\u00edsticas difiere sustancialmente entre poblaciones con intentos de suicidio y personas sin intentos. La aplicaci\u00f3n de los criterios de inclusi\u00f3n y exclusi\u00f3n produjo 1 millones 625 mil 350 sujetos de entrenamiento para los modelos 99% correspondieron a no casos y 16 mil a casos, es decir el 1%. Las variables capturadas m\u00e1s encontradas en el conjunto de datos fueron trastorno de control de impulsos, trastorno bipolar, trastorno esquizoafectivo y dependencia o abuso de opioides. De esta forma fue posible identificar variables de datos estructurados y no estructurados sobre pacientes con riesgo de suicidio o conductas suicidas. A ra\u00edz de estos datos es posible desarrollar estrategias eficaces de prevenci\u00f3n del suicidio. Consulta el estudio completo en el siguiente enlace: https:\/\/www.nature.com\/articles\/s41746-022-00558-0 BIBLIOGRAF\u00cdA NATURE https:\/\/www.nature.com\/articles\/s41746-022-00558-0<\/p>","protected":false},"author":1,"featured_media":24294,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,3393,160,1418],"tags":[145],"class_list":["post-24292","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\/24292","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=24292"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/24292\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/24294"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=24292"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=24292"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=24292"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}