{"id":6460,"date":"2019-11-18T04:23:59","date_gmt":"2019-11-18T04:23:59","guid":{"rendered":"https:\/\/www.kolabtree.com\/blog\/?p=6460"},"modified":"2019-11-27T10:47:24","modified_gmt":"2019-11-27T10:47:24","slug":"top-applications-of-machine-learning-in-healthcare","status":"publish","type":"post","link":"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/","title":{"rendered":"Principais aplica\u00e7\u00f5es da aprendizagem de m\u00e1quinas na \u00e1rea da sa\u00fade"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_45_1 counter-flat ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\">Tabela de Conte\u00fados<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" area-label=\"ez-toc-toggle-icon-1\"><label for=\"item-6a15b92abff03\" aria-label=\"Table of Content\"><span style=\"display: flex;align-items: center;width: 35px;height: 30px;justify-content: center;direction:ltr;\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/label><input  type=\"checkbox\" id=\"item-6a15b92abff03\"><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#Healthcare_Applications_of_Machine_Learning\" title=\"Aplica\u00e7\u00f5es da aprendizagem de m\u00e1quinas na \u00e1rea da sa\u00fade\">Aplica\u00e7\u00f5es da aprendizagem de m\u00e1quinas na \u00e1rea da sa\u00fade<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#1_The_Diagnosis_of_Heart_Disease\" title=\"1. O diagn\u00f3stico de doen\u00e7as card\u00edacas\">1. O diagn\u00f3stico de doen\u00e7as card\u00edacas<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#2_The_Prediction_of_Diabetes\" title=\"2. A Previs\u00e3o do Diabetes\">2. A Previs\u00e3o do Diabetes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#3_The_Prediction_of_Liver_Disease\" title=\"3. A previs\u00e3o da doen\u00e7a hep\u00e1tica\">3. A previs\u00e3o da doen\u00e7a hep\u00e1tica<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#4_ML_Applications_in_Surgery\" title=\"4. Aplica\u00e7\u00f5es ML em Cirurgia\">4. Aplica\u00e7\u00f5es ML em Cirurgia<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#5_The_Detection_of_Cancer\" title=\"5. A Detec\u00e7\u00e3o de C\u00e2ncer\">5. A Detec\u00e7\u00e3o de C\u00e2ncer<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#6_The_Discovery_of_New_Drugs\" title=\"6. A Descoberta de Novas Drogas\">6. A Descoberta de Novas Drogas<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#7_The_Personalization_of_Treatment\" title=\"7. A personaliza\u00e7\u00e3o do tratamento\">7. A personaliza\u00e7\u00e3o do tratamento<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/#Conclusion\" title=\"Conclus\u00e3o\">Conclus\u00e3o<\/a><\/li><\/ul><\/nav><\/div>\n<p><em>As aplica\u00e7\u00f5es de <a href=\"https:\/\/www.kolabtree.com\/find-an-expert\/subject\/machine-learning?utm_source=Blog&amp;utm_medium=Post&amp;utm_campaign=ML-Healthcare\">aprendizagem de m\u00e1quinas<\/a> in healthcare include detection and diagnosis of disease, drug discovery,\u00a0 and personalized medicine. Nicholas Walker describes how ML is being used to advance healthcare and medical <a href=\"https:\/\/www.kolabtree.com\/blog\/pt\/ensuring-reproducibility-in-ai-driven-research-how-freelance-experts-can-help-in-biotech-and-healthcare\/\">pesquisa<\/a>.\u00a0<\/em><\/p>\n<p><span style=\"font-weight: 400;\">O n\u00famero de pacientes nos hospitais est\u00e1 crescendo rapidamente, o que significa que est\u00e1 ficando cada vez mais desafiador analisar, e at\u00e9 mesmo registrar, todos os dados sobre os pacientes hoje em dia. Uma boa solu\u00e7\u00e3o para este problema \u00e9 o aprendizado de m\u00e1quinas, o que facilita a automatiza\u00e7\u00e3o da an\u00e1lise de dados e torna o sistema de sa\u00fade mais robusto. <a href=\"https:\/\/www.kolabtree.com\/find-an-expert\/subject\/machine-learning?utm_source=Blog&amp;utm_medium=Post&amp;utm_campaign=ML-Healthcare\">Aprendizagem da m\u00e1quina<\/a>A ci\u00eancia m\u00e9dica e a ci\u00eancia da computa\u00e7\u00e3o, como aplicada \u00e0 sa\u00fade, \u00e9 a conflu\u00eancia de dois campos: a ci\u00eancia m\u00e9dica e a ci\u00eancia da computa\u00e7\u00e3o. Esta alian\u00e7a tem permitido ao campo m\u00e9dico fazer enormes avan\u00e7os na \u00e1rea da sa\u00fade.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">H\u00e1 muitas pesquisas sendo realizadas nesta \u00e1rea. O Google, por exemplo, tem <a href=\"https:\/\/www.mercurynews.com\/2017\/03\/03\/google-computers-trained-to-detect-cancer\/\">inventou um <\/a><\/span><a href=\"https:\/\/www.mercurynews.com\/2017\/03\/03\/google-computers-trained-to-detect-cancer\/\"><span style=\"font-weight: 400;\">algoritmo<\/span><\/a><span style=\"font-weight: 400;\"> que detecta c\u00e9lulas cancer\u00edgenas. H\u00e1 muitos outros avan\u00e7os que tamb\u00e9m est\u00e3o sendo feitos, dos quais falaremos neste artigo.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Healthcare_Applications_of_Machine_Learning\"><\/span><b>Aplica\u00e7\u00f5es da aprendizagem de m\u00e1quinas na \u00e1rea da sa\u00fade<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">O objetivo da aprendizagem de m\u00e1quinas na ci\u00eancia da computa\u00e7\u00e3o \u00e9 tornar a m\u00e1quina mais eficiente e confi\u00e1vel. Na sa\u00fade, a m\u00e1quina \u00e9 uma extens\u00e3o e um multiplicador de for\u00e7a para o c\u00e9rebro do m\u00e9dico. Afinal, um paciente sempre precisar\u00e1 do toque e cuidado de um ser humano, o que uma m\u00e1quina n\u00e3o pode proporcionar. O trabalho de uma m\u00e1quina, portanto, n\u00e3o \u00e9 para substituir o m\u00e9dico, mas para ajud\u00e1-lo a prestar um melhor servi\u00e7o e cuidado.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"1_The_Diagnosis_of_Heart_Disease\"><\/span><b>1. O diagn\u00f3stico de doen\u00e7as card\u00edacas<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">O cora\u00e7\u00e3o \u00e9 um dos principais \u00f3rg\u00e3os de nosso corpo. H\u00e1 uma variedade de doen\u00e7as card\u00edacas de que sofremos, tais como doen\u00e7a coron\u00e1ria, doen\u00e7a arterial coron\u00e1ria, etc. Os pesquisadores est\u00e3o desenvolvendo algoritmos de aprendizagem de m\u00e1quinas para facilitar o diagn\u00f3stico de doen\u00e7as card\u00edacas. \u00c9 um t\u00f3pico altamente pesquisado globalmente e um sistema automatizado para o diagn\u00f3stico de doen\u00e7as card\u00edacas seria uma das maiores proezas da realiza\u00e7\u00e3o humana nos 21<\/span><span style=\"font-weight: 400;\">st<\/span><span style=\"font-weight: 400;\"> s\u00e9culo.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>Os pesquisadores est\u00e3o trabalhando em M\u00e1quinas Vetoriais de Apoio, Na\u00efve Bayes e outras formas de algoritmos de aprendizagem supervisionada de m\u00e1quinas para resolver o problema de detec\u00e7\u00e3o e diagn\u00f3stico de doen\u00e7as card\u00edacas.<\/strong> Um dos conjuntos de dados mais importantes neste campo \u00e9 o de <\/span><a href=\"https:\/\/archive.ics.uci.edu\/ml\/datasets\/Heart+Disease\"><span style=\"font-weight: 400;\">UCI<\/span><\/a><span style=\"font-weight: 400;\">O que pode ser usado para treinar algoritmos.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"2_The_Prediction_of_Diabetes\"><\/span><b>2. A Previs\u00e3o do Diabetes<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">O diabetes n\u00e3o \u00e9 apenas uma doen\u00e7a perigosa, mas \u00e9 tamb\u00e9m uma das doen\u00e7as mais comuns no mundo. \u00c9 tamb\u00e9m uma doen\u00e7a de portal, sendo ela mesma uma das principais causas de outras doen\u00e7as e levando suas v\u00edtimas inexoravelmente em dire\u00e7\u00e3o \u00e0 morte.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">O diabetes tem a capacidade de danificar v\u00e1rias partes do corpo, tais como o cora\u00e7\u00e3o, o rim e o sistema nervoso. O aprendizado da m\u00e1quina est\u00e1 sendo examinado como uma forma de detectar os marcadores do diabetes suficientemente cedo para que as vidas dos pacientes possam ser salvas.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">H\u00e1 muitos algoritmos que podem ser usados para prever o diabetes, incluindo Na\u00efve Bayes, Decision Trees, Random Forests, e KNNs. O Na\u00efve Bayes supera os outros quando se trata de precis\u00e3o, devido ao seu bom desempenho e ao pouco tempo que leva para fazer c\u00e1lculos.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"3_The_Prediction_of_Liver_Disease\"><\/span><b>3. A previs\u00e3o da doen\u00e7a hep\u00e1tica<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">O f\u00edgado \u00e9 mais um \u00f3rg\u00e3o que est\u00e1 entre os \u00f3rg\u00e3os prim\u00e1rios do corpo. \u00c9 crucial para o metabolismo e pode ser atacado por uma s\u00e9rie de doen\u00e7as, incluindo c\u00e2ncer hep\u00e1tico, hepatite cr\u00f4nica, cirrose hep\u00e1tica, e muitas outras.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Os conceitos de minera\u00e7\u00e3o de dados e aprendizagem de m\u00e1quinas entraram recentemente em jogo na busca de um sistema de previs\u00e3o de doen\u00e7as hep\u00e1ticas. Para ser honesto, \u00e9 um esfor\u00e7o bastante desafiador tentar prever doen\u00e7as hep\u00e1ticas, em parte porque h\u00e1 tantas doen\u00e7as poss\u00edveis que poderiam atacar o f\u00edgado e tamb\u00e9m em parte porque h\u00e1 um volume t\u00e3o grande de dados sobre o assunto.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Os pesquisadores, no entanto, est\u00e3o fazendo o melhor que podem para contornar estas quest\u00f5es. Muito tem sido escrito por v\u00e1rios <\/span><a href=\"https:\/\/edubirdie.com\/\"><span style=\"font-weight: 400;\">servi\u00e7os de reda\u00e7\u00e3o de ensaios nos estados unidos<\/span><\/a><span style=\"font-weight: 400;\"> sobre o uso de t\u00e9cnicas de aprendizagem de m\u00e1quinas como agrupamento, classifica\u00e7\u00e3o, e assim por diante. H\u00e1 tamb\u00e9m conjuntos de dados dispon\u00edveis que os pesquisadores est\u00e3o usando para desenvolver seus algoritmos.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"4_ML_Applications_in_Surgery\"><\/span><b>4. Aplica\u00e7\u00f5es ML em Cirurgia<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.kolabtree.com\/find-an-expert\/subject\/surgery?utm_source=Blog&amp;utm_medium=Post&amp;utm_campaign=ML-Healthcare\">Cirurgia<\/a>Em particular a cirurgia rob\u00f3tica, \u00e9 uma das aplica\u00e7\u00f5es mais promissoras da aprendizagem de m\u00e1quinas na \u00e1rea da sa\u00fade. N\u00e3o \u00e9 apenas um grande campo, mas uma categoria guarda-chuva com cerca de 4 subcampos: avalia\u00e7\u00e3o de habilidades cir\u00fargicas, sutura autom\u00e1tica, modelagem de fluxo de trabalho cir\u00fargico e melhoria de materiais cir\u00fargicos rob\u00f3ticos.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A sutura \u00e9 o processo de costura de uma ferida. Quando \u00e9 autom\u00e1tica, faz com que o procedimento cir\u00fargico demore muito menos tempo e alivia o estresse do cirurgi\u00e3o. Os pesquisadores est\u00e3o trabalhando muito neste campo, aplicando os princ\u00edpios da aprendizagem da m\u00e1quina aos diferentes aspectos da cirurgia e trabalhando para um futuro onde a cirurgia assistida por rob\u00f4s seja eficaz e segura, e talvez at\u00e9 minimamente invasiva.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Na neurocirurgia, por exemplo, os rob\u00f4s ainda n\u00e3o s\u00e3o t\u00e3o eficazes quanto os neurocirurgi\u00f5es gostariam que fossem. Como resultado, praticamente todos os procedimentos s\u00e3o manuais e todo o processo \u00e9 bastante demorado. Tamb\u00e9m n\u00e3o h\u00e1 nenhum feedback autom\u00e1tico. O desenvolvimento da aprendizagem da m\u00e1quina neste campo se revelar\u00e1 muito ben\u00e9fico.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"5_The_Detection_of_Cancer\"><\/span><b>5. A Detec\u00e7\u00e3o de C\u00e2ncer<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">A aprendizagem de m\u00e1quinas e suas diferentes abordagens est\u00e3o sendo amplamente utilizadas para prever e detectar v\u00e1rios tipos de tumores. O aprendizado profundo tamb\u00e9m \u00e9 muito importante neste campo, pois n\u00e3o h\u00e1 falta de dados e o m\u00e9todo \u00e9 acess\u00edvel. De fato, o aprendizado profundo tem sido bastante bem sucedido no diagn\u00f3stico do c\u00e2ncer de mama e tem aumentado muito a precis\u00e3o nesse campo.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><strong>DeepGene, um classificador de aprendizagem profunda para tipos de c\u00e2ncer, tem sido amplamente explorado por pesquisadores chineses.<\/strong> Uma das formas mais promissoras de prever o c\u00e2ncer a que a m\u00e1quina e o aprendizado profundo est\u00e3o sendo aplicados \u00e9 a extra\u00e7\u00e3o de caracter\u00edsticas dos dados sobre a express\u00e3o g\u00eanica. Esta abordagem se presta especialmente bem a redes neurais enroladas, um tipo de algoritmo de aprendizagem de m\u00e1quina.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"6_The_Discovery_of_New_Drugs\"><\/span><b>6. A Descoberta de Novas Drogas<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">O aprendizado de m\u00e1quinas est\u00e1 sendo amplamente utilizado na descoberta de drogas e est\u00e1 se mostrando bastante promissor. <strong>A Microsoft tem o Projeto Hanover, que est\u00e1 procurando melhorar <a href=\"https:\/\/www.kolabtree.com\/find-an-expert\/subject\/precision-medicine?utm_source=Blog&amp;utm_medium=Post&amp;utm_campaign=ML-Healthcare\">medicina de precis\u00e3o<\/a> utilizando t\u00e9cnicas de aprendizagem de m\u00e1quinas.<\/strong> H\u00e1 v\u00e1rias outras empresas trabalhando no mesmo projeto, todas elas utilizando diferentes abordagens promissoras para o problema.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">O aprendizado de m\u00e1quinas apresenta v\u00e1rios benef\u00edcios quando aplicado \u00e0 ci\u00eancia da sa\u00fade. Tornar\u00e1 o processo de descoberta de novos medicamentos mais r\u00e1pido e tamb\u00e9m menos propenso a erros, diminuindo drasticamente a taxa de falhas. Tamb\u00e9m reduzir\u00e1 o custo da descoberta de medicamentos, otimizando o processo de fabrica\u00e7\u00e3o dos mesmos.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"7_The_Personalization_of_Treatment\"><\/span><b>7. A personaliza\u00e7\u00e3o do tratamento<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">A aprendizagem de m\u00e1quinas aplicada \u00e0 personaliza\u00e7\u00e3o do tratamento \u00e9 uma das \u00e1reas mais pesquisadas tanto na \u00e1rea da sa\u00fade quanto na aprendizagem de m\u00e1quinas. O objetivo do tratamento personalizado \u00e9 poder melhorar os servi\u00e7os individuais de sa\u00fade utilizando dados altamente individuais e t\u00e9cnicas anal\u00edticas. Ferramentas de aprendizagem de m\u00e1quinas para computa\u00e7\u00e3o e estat\u00edstica s\u00e3o utilizadas nesta \u00e1rea para desenvolver sistemas de tratamento personalizados baseados na informa\u00e7\u00e3o gen\u00e9tica e nos sintomas do paciente.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Algoritmos de aprendizagem supervisionada da m\u00e1quina s\u00e3o utilizados no desenvolvimento de sistemas de tratamento personalizados utilizando informa\u00e7\u00f5es m\u00e9dicas individuais dos pacientes.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclus\u00e3o<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">As aplica\u00e7\u00f5es da aprendizagem de m\u00e1quinas na \u00e1rea da sa\u00fade est\u00e3o ajudando a desenvolver e fornecer medicamentos personalizados, melhorar a qualidade de vida e detectar doen\u00e7as desde cedo. O futuro \u00e9 ao mesmo tempo promissor e brilhante. O aprendizado de m\u00e1quinas promete fazer avan\u00e7ar a sa\u00fade a n\u00edveis que talvez n\u00e3o possamos imaginar hoje. No futuro, o poder dos computadores poder\u00e1 ser exercido sobre as enfermidades f\u00edsicas da humanidade, fazendo de n\u00f3s seres verdadeiramente imortais.\u00a0<\/span><\/p>\n<p>Precisa de ajuda com um projeto de aprendizagem de m\u00e1quinas?  Contrate <a href=\"https:\/\/www.kolabtree.com\/find-an-expert\/subject\/machine-learning?utm_source=Blog&amp;utm_medium=Post&amp;utm_campaign=ML-Healthcare\">consultores de aprendizagem de m\u00e1quinas freelance<\/a> em Kolabtree. \u00c9 gr\u00e1tis para postar seu projeto e receber or\u00e7amentos.<\/p>","protected":false},"excerpt":{"rendered":"<p>The applications of machine learning in healthcare include detection and diagnosis of disease, drug discovery,\u00a0 and personalized medicine. Nicholas Walker describes how ML is being used to advance healthcare and medical research.\u00a0 The number of patients in hospitals is growing rapidly, which means it\u2019s getting more and more challenging to analyze, and even record, all<\/p>\n<div class=\"read-more\"><a href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/\" title=\"Leia mais\">Leia mais<\/a><\/div>","protected":false},"author":12,"featured_media":6472,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[443,433],"tags":[355,336,497],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.1 (Yoast SEO v20.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Top Applications of Machine Learning in Healthcare - The Kolabtree Blog<\/title>\n<meta name=\"description\" content=\"The application of machine learning in healthcare is helping to detect and predict diseases easily and deliver personalized medical treatments.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top Applications of Machine Learning in Healthcare\" \/>\n<meta property=\"og:description\" content=\"The application of machine learning in healthcare is helping to detect and predict diseases easily and deliver personalized medical treatments.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.kolabtree.com\/blog\/pt\/top-applications-of-machine-learning-in-healthcare\/\" \/>\n<meta property=\"og:site_name\" content=\"The Kolabtree Blog\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/kolabtree\" \/>\n<meta property=\"article:published_time\" content=\"2019-11-18T04:23:59+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2019-11-27T10:47:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.kolabtree.com\/blog\/wp-content\/uploads\/2019\/11\/machine-learning-applications-healthcare.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1280\" \/>\n\t<meta property=\"og:image:height\" content=\"800\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Ramya Sriram\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@kolabtree\" \/>\n<meta name=\"twitter:site\" content=\"@kolabtree\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"Ramya Sriram\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutos\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Top Applications of Machine Learning in Healthcare - 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