{"id":271369,"date":"2026-06-12T12:51:00","date_gmt":"2026-06-12T16:51:00","guid":{"rendered":"https:\/\/news-you-need.com\/index.php\/2026\/06\/12\/accurate-surgery-time-prediction-astp-strategy-based-on-artificial-intelligence-techniques\/"},"modified":"2026-06-12T13:10:32","modified_gmt":"2026-06-12T17:10:32","slug":"accurate-surgery-time-prediction-astp-strategy-based-on-artificial-intelligence-techniques","status":"publish","type":"post","link":"https:\/\/news-you-need.com\/index.php\/2026\/06\/12\/accurate-surgery-time-prediction-astp-strategy-based-on-artificial-intelligence-techniques\/","title":{"rendered":"Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques"},"content":{"rendered":"<p><a href=\"https:\/\/www.nature.com\/articles\/s41598-026-55198-1\">Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques<\/a><\/p>\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41598-026-55198-1\">https:\/\/www.nature.com\/articles\/s41598-026-55198-1<\/a><\/p>\n<p>Publish Date: <a href=\"publish_date]\">2026-06-12 12:51:00<\/a><\/p>\n<p>Source Domain: <a href=\"www.nature.com\">www.nature.com<\/a><\/p>\n<p>Our strategy consists of two layers as illustrated in Fig.\u00a01. The preprocessing layer performs Mixed-Scaling, computes feature importance using LSTM and Random Forest, and Feature ranking, which orders the features and selects the most informative subset. The prediction layer applies HGBR to the ranked features, yielding the final time-to-surgery prediction.<\/p>\n<h3 class=\"c-article__sub-heading\" id=\"Sec4\">Preprocessing layer in ASTP<\/h3>\n<p>In this layer, the data is processed using One Hot encoding, Mixed-Scaling, and determining the importance and ranking of features.<\/p>\n<h4 class=\"c-article__sub-heading c-article__sub-heading--small\" id=\"Sec5\">One hot encoding<\/h4>\n<p>In tabular data modeling, each categorical value in Eq.\u00a01 is represented by a one-hot vector of length K (i.e., a <span class=\"mathjax-tex\">(:times:)<\/span> K), where K is the number of possible classes for that categories20. The vector contains zeros in all cells except one with the value 1, which specifies the actual class.<\/p>\n<p><span class=\"mathjax-tex\">$$:{left[varnothing::right(xleft)right]}_{k}=1:left{x={c}_{k}right},:::::::::::k=1,dots:..,K,::::::::::::::sum:_{k}^{K}{left[varnothing::right(xleft)right]}_{k}=1$$<\/span><\/p>\n<p>\n                    (1)\n                <\/p>\n<p><span class=\"mathjax-tex\">(:c={:{c}_{1},dots:dots:.,{c}_{k}})<\/span> Is the category set (with size K), x <span class=\"mathjax-tex\">(:epsilon)<\/span> c is observed category, <span class=\"mathjax-tex\">(:{left[{varnothing}:right(xleft)right]}_{k}:)<\/span>denotes the K-th coordinate of the one-hot vector, 1{<span class=\"stix\">\u22c5<\/span>} is the indicator function (1 if the condition holds, 0 otherwise), n is the number of samples, the one hot block is O <span class=\"mathjax-tex\">(:epsilon)<\/span> <span class=\"mathjax-tex\">(:{left{text{0,1}right}}^{ntimes:k})<\/span> in Eq.\u00a02.<\/p>\n<p><span class=\"mathjax-tex\">$$:{O}_{ik}=1:{:{x}_{i}=:{c}_{k}}$$<\/span><\/p>\n<p>\n                    (2)\n                <\/p>\n<p>This formulation ensures that the learning model does not assume that \u201clarger numbers are more important,\u201d because simple numerical notations (e.g., \u201cstable\u2009=\u20091, more stable\u2009=\u20092, unstable\u2009=\u20093\u201d) might imply an unreal rank relationship or distance between classes. For example, the value \u201cemerging\u201d is not \u201cmore important\u201d than \u201cscheduled\u201d because we write it as 2&#8230;<\/p>\n<p><a href=\"https:\/\/www.nature.com\/articles\/s41598-026-55198-1\">Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques https:\/\/www.nature.com\/articles\/s41598-026-55198-1 Publish Date: 2026-06-12&#8230;<\/p>\n","protected":false},"author":1,"featured_media":271371,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"https:\/\/media.springernature.com\/m685\/springer-static\/image\/art%3A10.1038%2Fs41598-026-55198-1\/MediaObjects\/41598_2026_55198_Fig1_HTML.png","fifu_image_alt":"","footnotes":""},"categories":[14],"tags":[],"class_list":["post-271369","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"_links":{"self":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/271369"}],"collection":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/comments?post=271369"}],"version-history":[{"count":1,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/271369\/revisions"}],"predecessor-version":[{"id":271373,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/posts\/271369\/revisions\/271373"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/media\/271371"}],"wp:attachment":[{"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/media?parent=271369"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/categories?post=271369"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/news-you-need.com\/index.php\/wp-json\/wp\/v2\/tags?post=271369"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}