{"id":710,"date":"2021-04-15T17:30:58","date_gmt":"2021-04-15T15:30:58","guid":{"rendered":"http:\/\/www.labdma.unina.it\/?p=710"},"modified":"2021-11-15T17:31:10","modified_gmt":"2021-11-15T16:31:10","slug":"ai-and-healthcare","status":"publish","type":"post","link":"https:\/\/www.labdma.unina.it\/index.php\/2021\/04\/15\/ai-and-healthcare\/","title":{"rendered":"AI and Healthcare"},"content":{"rendered":"\n<p>NEW PUBLICATION &#8211; <strong><em>Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion<\/em><\/strong> is the title of the article published by M.O.D.A.L. on Information Fusion, Elsevier.<\/p>\n\n\n\n<p> This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature\u00a0compression stage\u00a0which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach\u2019s effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy. <\/p>\n\n\n\n<p>Link to Publication:<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253521000592\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253521000592<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<div class=\"slide-text-bg2\">\n<h3>&lt;div class=&quot;slide-text-bg2&quot;&gt;<br \/>\n&lt;h3&gt;NEW PUBLICATION &#8211; Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion is the title of the&lt;\/h3&gt;<br \/>\n&lt;\/div&gt;<br \/>\n&lt;div class=&quot;flex-btn-div&quot;&gt;&lt;a href=&quot;https:\/\/www.labdma.unina.it\/index.php\/2021\/04\/15\/ai-and-healthcare\/&quot; class=&quot;btn1 flex-btn&quot;&gt;Leggi tutto&lt;\/a&gt;&lt;\/div&gt;<br \/>\n<\/h3>\n<\/div>\n<div class=\"flex-btn-div\"><a href=\"https:\/\/www.labdma.unina.it\/index.php\/2021\/04\/15\/ai-and-healthcare\/\" class=\"btn1 flex-btn\">Leggi tutto<\/a><\/div>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/posts\/710"}],"collection":[{"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/comments?post=710"}],"version-history":[{"count":1,"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/posts\/710\/revisions"}],"predecessor-version":[{"id":711,"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/posts\/710\/revisions\/711"}],"wp:attachment":[{"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/media?parent=710"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/categories?post=710"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.labdma.unina.it\/index.php\/wp-json\/wp\/v2\/tags?post=710"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}