{"id":574171,"date":"2025-09-22T14:24:08","date_gmt":"2025-09-22T18:24:08","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ece\/?post_type=news&#038;p=574171"},"modified":"2025-09-22T14:24:08","modified_gmt":"2025-09-22T18:24:08","slug":"predicting-deadly-complications-after-surgery","status":"publish","type":"news","link":"https:\/\/engineering.jhu.edu\/ece\/news\/predicting-deadly-complications-after-surgery\/","title":{"rendered":"Predicting Deadly Complications After Surgery"},"content":{"rendered":"<p>A new artificial intelligence model found previously undetected signals in routine heart tests that strongly predict which patients will suffer potentially deadly complications after surgery. The model significantly outperformed risk scores currently relied upon by doctors.<\/p>\n<p>The federally funded work by Johns Hopkins University researchers, which turns standard and inexpensive test results into a potentially lifesaving tool, could transform decision-making and risk calculation for both patients and surgeons.<\/p>\n<p>&#8220;We demonstrate that a basic electrocardiogram contains important prognostic information not identifiable by the naked eye,&#8221; said senior author <a href=\"https:\/\/profiles.hopkinsmedicine.org\/provider\/robert-david-stevens\/2702555\">Robert D. Stevens<\/a>, chief of the <a href=\"https:\/\/anesthesiology.hopkinsmedicine.org\/integration-and-information-management\/\">Division of Informatics, Integration, and Innovation at Johns Hopkins Medicine<\/a>. &#8220;We can only extract it with machine learning techniques.&#8221;<\/p>\n<p>The findings are published today in the <em>British Journal of Anaesthesia<\/em>.<\/p>\n<p><a href=\"https:\/\/hub.jhu.edu\/2025\/09\/17\/artificial-intelligence-predicts-post-surgery-complications\/\">Read the full article on the Hub.<\/a><\/p>\n","protected":false},"template":"","class_list":["post-574171","news","type-news","status-publish","hentry","news_categories-research"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predicting Deadly Complications After Surgery - Department of Electrical and Computer Engineering<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/ece\/news\/predicting-deadly-complications-after-surgery\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting Deadly Complications After Surgery - Department of Electrical and Computer Engineering\" \/>\n<meta property=\"og:description\" content=\"A new artificial intelligence model found previously undetected signals in routine heart tests that strongly predict which patients will suffer potentially deadly complications after surgery. The model significantly outperformed risk&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/ece\/news\/predicting-deadly-complications-after-surgery\/\" \/>\n<meta property=\"og:site_name\" content=\"Department of Electrical and Computer Engineering\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Predicting Deadly Complications After Surgery - Department of Electrical and Computer Engineering","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/engineering.jhu.edu\/ece\/news\/predicting-deadly-complications-after-surgery\/","og_locale":"en_US","og_type":"article","og_title":"Predicting Deadly Complications After Surgery - Department of Electrical and Computer Engineering","og_description":"A new artificial intelligence model found previously undetected signals in routine heart tests that strongly predict which patients will suffer potentially deadly complications after surgery. 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