{"id":27060,"date":"2022-06-06T08:51:04","date_gmt":"2022-06-06T13:51:04","guid":{"rendered":"https:\/\/saluddigital.com\/?p=27060"},"modified":"2025-10-20T11:51:02","modified_gmt":"2025-10-20T17:51:02","slug":"modelo-detecta-con-precision-el-abuso-de-sustancias-a-traves-de-notas-clinicas-en-expedientes-clinicos-electronicos","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/big-data\/modelo-detecta-con-precision-el-abuso-de-sustancias-a-traves-de-notas-clinicas-en-expedientes-clinicos-electronicos\/","title":{"rendered":"Model accurately detects substance abuse through clinical notes in electronic medical records"},"content":{"rendered":"
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Substance abuse is prevalent in hospital settings and derives from various behavioral conditions, it is also a problem that is difficult to identify in a comprehensive and reliable manner.<\/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 number of hospital visits related to substance use in the United States increased during the pandemic, and although it is not a recent situation, there are not enough tools to detect these types of conditions. For this reason, researchers in the United States developed an Artificial Intelligence (AI) model to address this situation and published a study that offers details about an algorithm they developed to identify substance abuse through Artificial Intelligence.<\/p>

The study titled \u201cDevelopment and Multimodal Validation of a Substance Abuse Treatment Referral Algorithm Using Artificial Intelligence (SMART-AI): A Deep Learning Retrospective Study,\u201d aimed to use natural language processing (NLP) and clinical notes in electronic medical records to accurately detect substance abuse.<\/p>

The model presented in the study was trained on a data set belonging to the Rush University Medical Center in Chicago, with data available from 2017 to December 31, 2019. The key focus for the development of this tool was the notes of ECE performed during the patient's first 24 hours, these were assigned standardized medical vocabulary and were classified with neural network model labels.<\/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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"A machine learning approach treats each word as a feature and does not require subject matter expertise, unlike models that rely on keywords and rule-based systems," the study details.<\/p>

The model was trained in a cohort of 1,912 patients with 3,5% records of abuse with any type of substance, of which 202 had abuse of more than one type of substance.<\/p>

\u201cWe developed and then temporarily and externally validated an accurate screening tool to identify cases of substance abuse. Our model showed good calibration and face validity. With this model, we offer an automated approach to detect multiple types of substance abuse simultaneously\u201d, explains the study.<\/p>

You can consult the full study at the following link: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00041-3\/fulltext<\/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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THE LANCET<\/strong><\/p>

https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00041-3\/fulltext<\/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 abuso de sustancias es prevalente en entornos hospitalarios y deriva de diversas condiciones conductuales, adem\u00e1s es una problem\u00e1tica dif\u00edcil de identificar de manera integral y confiable. El n\u00famero de visitas al hospital relacionadas con el uso de sustancias en Estados Unidos aument\u00f3 durante la pandemia, y a pesar de ser no ser una situaci\u00f3n reciente, no hay suficientes herramientas para la detecci\u00f3n de este tipo de condiciones. Por ello investigadores en Estados Unidos, desarrollaron un modelo de Inteligencia Artificial (IA) para atender esta situaci\u00f3n y publicaron un estudio que ofrece detalles sobre un algoritmo que desarrollaron para identificar el uso indebido de sustancias a trav\u00e9s de Inteligencia Artificial. El estudio titulado \u201cDesarrollo y validaci\u00f3n multimodal de un algoritmo de uso indebido de sustancias para derivaci\u00f3n a tratamiento utilizando inteligencia artificial (SMART-AI): un estudio retrospectivo de aprendizaje profundo\u201d, tuvo como objetivo utilizar el procesamiento de lenguaje natural (PLN)y notas cl\u00ednicas en registros cl\u00ednicos electr\u00f3nicos para detectar con precisi\u00f3n el abuso de sustancias. El modelo presentado en el estudio, fue entrenado en un conjunto de datos perteneciente al Rush University Medical Center de Chicago, con datos disponibles desde 2017 hasta el 31 de diciembre de 2019. El enfoque clave para el desarrollo de esta herramienta, fueron las notas de ECE realizadas durante las primeras 24 horas del paciente, a estas les fue asignado vocabulario m\u00e9dico estandarizado y se clasificaron con etiquetas de modelos de una red neuronal. \u201cUn enfoque de aprendizaje autom\u00e1tico trata cada palabra como una caracter\u00edstica y no requiere experiencia en la materia, a diferencia de los modelos que se basan en palabras clave y sistemas basados \u200b\u200ben reglas\u201d, detalla el estudio. El modelo fue entrenado en una cohorte de 1,912 pacientes con registros del 3,5% de abuso con cualquier tipo de sustancias, de los cuales 202 ten\u00edan abuso a m\u00e1s de un tipo de sustancias. \u201cDesarrollamos y luego validamos temporal y externamente una herramienta de detecci\u00f3n precisa para identificar casos de abuso de sustancias. Nuestro modelo mostr\u00f3 una buena calibraci\u00f3n y validez aparente. Con este modelo, ofrecemos un enfoque automatizado para detectar m\u00faltiples tipos de abuso de sustancias simult\u00e1neamente\u201d, explica el estudio. Puedes consultar el estudio completo en el siguiente enlace: https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00041-3\/fulltext BIBLIOGRAF\u00cdA THE LANCET https:\/\/www.thelancet.com\/journals\/landig\/article\/PIIS2589-7500(22)00041-3\/fulltext<\/p>","protected":false},"author":1,"featured_media":27062,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3399,156,160],"tags":[145],"class_list":["post-27060","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\/27060","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=27060"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/27060\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/27062"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=27060"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=27060"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=27060"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}