{"id":124038,"date":"2024-11-10T00:42:50","date_gmt":"2024-11-09T17:42:50","guid":{"rendered":"https:\/\/hotvideos24.online\/?p=124038"},"modified":"2024-11-10T00:42:50","modified_gmt":"2024-11-09T17:42:50","slug":"ai-tool-reveals-long-covid-may-affect-23-of-people","status":"publish","type":"post","link":"https:\/\/hotvideos24.online\/?p=124038","title":{"rendered":"AI Tool Reveals Long COVID May Affect 23% of People"},"content":{"rendered":"<p> <script async src=\"https:\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js?client=ca-pub-3711241968723425\"\r\n     crossorigin=\"anonymous\"><\/script>\r\n<ins class=\"adsbygoogle\"\r\n     style=\"display:block\"\r\n     data-ad-format=\"fluid\"\r\n     data-ad-layout-key=\"-fb+5w+4e-db+86\"\r\n     data-ad-client=\"ca-pub-3711241968723425\"\r\n     data-ad-slot=\"7910942971\"><\/ins>\r\n<script>\r\n     (adsbygoogle = window.adsbygoogle || []).push({});\r\n<\/script><br \/>\n<\/p>\n<div>\n<p><strong>Summary: <\/strong>A new AI tool identified long COVID in 22.8% of patients, a much higher rate than previously diagnosed. By analyzing extensive health records from nearly 300,000 patients, the algorithm identifies long COVID by distinguishing symptoms linked specifically to SARS-CoV-2 infection rather than pre-existing conditions.<\/p>\n<p>This AI approach, known as \u201cprecision phenotyping,\u201d helps clinicians differentiate long COVID symptoms from other health issues and may improve diagnostic accuracy by about 3%.<\/p>\n<p><strong>Key Facts:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>AI-based precision phenotyping<\/strong>: Identifies long COVID only after excluding other causes of symptoms in health records, improving diagnostic accuracy.<\/li>\n<li><strong>Broader representation<\/strong>: Algorithm diagnoses mirror the Massachusetts demographic profile, addressing biases found in traditional diagnostic codes.<\/li>\n<li><strong>Research potential<\/strong>: Algorithm may advance future research on the genetic and biochemical factors of long COVID subtypes.<\/li>\n<\/ul>\n<p><strong>Source: <\/strong>Harvard<\/p>\n<p><strong>While earlier diagnostic studies have suggested that 7 percent of the population suffers from long COVID, a new AI tool developed by \u00a0Mass General Brigham\u00a0revealed a much higher 22.8 percent, according to the study.\u00a0<\/strong><\/p>\n<p>The AI-based tool can sift through electronic health records to help clinicians identify cases of long COVID. The often-mysterious condition can encompass a\u00a0litany of\u00a0enduring symptoms,\u00a0including fatigue, chronic cough, and brain fog after infection from SARS-CoV-2.\u00a0<\/p>\n<p>The algorithm used was developed by drawing de-identified patient data from the clinical records of nearly 300,000 patients across 14 hospitals and 20 community health centers in the Mass General Brigham system.<\/p>\n<figure class=\"wp-block-image size-full\"><picture fetchpriority=\"high\" decoding=\"async\" class=\"wp-image-105998\"><source type=\"image\/webp\" srcset=\"https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience.jpg.webp 1200w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-770x513.jpg.webp 770w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-1155x770.jpg.webp 1155w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-370x247.jpg.webp 370w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-293x195.jpg.webp 293w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-150x100.jpg.webp 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\"\/><img fetchpriority=\"high\" decoding=\"async\" width=\"1200\" height=\"800\" src=\"https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience.jpg\" alt=\"This shows people.\" srcset=\"https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience.jpg 1200w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-300x200.jpg 300w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-770x513.jpg 770w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-1155x770.jpg 1155w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-370x247.jpg 370w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-293x195.jpg 293w, https:\/\/neurosciencenews.com\/files\/2024\/11\/ai-long-covid-neuroscience-150x100.jpg 150w\" sizes=\"(max-width: 1200px) 100vw, 1200px\"\/> <\/picture><figcaption class=\"wp-element-caption\">The researchers said their tool is about 3 percent more accurate than the data ICD-10 codes capture, while being less biased. Credit: Neuroscience News<\/figcaption><\/figure>\n<p>The\u00a0results,\u00a0published in the journal\u00a0<em>MedRxiv<\/em>, could identify more people who should be receiving care for this potentially debilitating condition.<\/p>\n<p>\u201cOur AI tool could turn a foggy diagnostic process into something sharp and focused, giving clinicians the power to make sense of a challenging condition,\u201d said senior author\u00a0Hossein Estiri, head of AI Research at the Center for AI and Biomedical Informatics of the Learning Healthcare System (CAIBILS) at MGB and an associate professor of medicine at Harvard Medical School.\u00a0<\/p>\n<p>\u201cWith this work, we may finally be able to see long COVID for what it truly is \u2014 and more importantly, how to treat it.\u201d<\/p>\n<p>For the purposes of their study, Estiri and colleagues defined long COVID as a\u00a0diagnosis of exclusion\u00a0that is also\u00a0infection-associated. That means the diagnosis could not be explained in the patient\u2019s unique medical record but was associated with a COVID infection. In addition, the diagnosis needed to have persisted for two months or longer in a 12-month follow up window.\u00a0<\/p>\n<p>The novel method developed by Estiri and colleagues, called \u201cprecision phenotyping,\u201d sifts through individual records to identify symptoms and conditions linked to COVID-19 to track symptoms over time in order to differentiate them from other illnesses.<\/p>\n<p>For example, the algorithm can detect if shortness of breath results from pre-existing conditions like heart failure or asthma rather than long COVID. Only when every other possibility was exhausted would the tool flag the patient as having long COVID.\u00a0<\/p>\n<p>\u201cPhysicians are often faced with having to wade through a tangled web of symptoms and medical histories, unsure of which threads to pull, while balancing busy caseloads. Having a tool powered by AI that can methodically do it for them could be a game-changer,\u201d said\u00a0Alaleh Azhir, co-lead author and an\u00a0internal medicine resident at Brigham and Women\u2019s Hospital, a founding member of the Mass General Brigham healthcare system.<\/p>\n<p>The new tool\u2019s patient-centered diagnoses may also help alleviate biases built into current diagnostics for long COVID, said researchers, who noted diagnoses with the official ICD-10 diagnostic code for long COVID trend toward those with easier access to healthcare.<\/p>\n<p>The researchers said their tool is about 3 percent more accurate than the data ICD-10 codes capture, while being less biased. Specifically, their study demonstrated that the individuals they identified as having long COVID mirror the broader demographic makeup of Massachusetts, unlike long COVID algorithms that rely on a single diagnostic code or individual clinical encounters, skewing results toward certain populations such as those with more access to care.<\/p>\n<p>\u201cThis broader scope ensures that marginalized communities, often sidelined in clinical studies, are no longer invisible,\u201d said Estiri.<\/p>\n<p>Limitations of the study and AI tool include that health record data the algorithm uses to account for long COVID symptoms may be less complete than the data physicians capture in post-visit clinical notes.<\/p>\n<p>Another limitation was the algorithm did not capture possible worsening of a prior condition that may have been a long COVID symptom. For example, if a patient had COPD that worsened before they developed COVID-19, the algorithm might have removed the episodes even if they were long COVID indicators.<\/p>\n<p>Declines in COVID-19 testing in recent years also makes it difficult to identify when a patient may have first gotten COVID-19.<\/p>\n<p>The study was limited to patients in Massachusetts.<\/p>\n<p>Future studies may explore the algorithm in cohorts of patients with specific conditions, like COPD or diabetes. The researchers also plan to release this algorithm publicly on open access so physicians and healthcare systems globally can use it in their patient populations.\u00a0<\/p>\n<p>In addition to opening the door to better clinical care, this work may lay the foundation for future research into the genetic and biochemical factors behind long COVID\u2019s various subtypes.<\/p>\n<p>\u201cQuestions about the true burden of long COVID \u2014 questions that have thus far remained elusive \u2014 now seem more within reach,\u201d said Estiri.<\/p>\n<p><strong>Funding: <\/strong>Support was given by the National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIAID) R01AI165535, National Heart, Lung, and Blood Institute (NHLBI) OT2HL161847, and National Center for Advancing Translational Sciences (NCATS) UL1 TR003167, UL1 TR001881, and U24TR004111.<\/p>\n<p>J. H\u00fcgel\u2019s work was partially funded by a fellowship within the IFI program of the German Academic Exchange Service (DAAD) and by the Federal Ministry of Education and Research (BMBF) as well by the German Research Foundation (426671079).<\/p>\n<h2 class=\"wp-block-heading\">About this AI and long COVID research news<\/h2>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Author: <\/strong><a href=\"https:\/\/news.harvard.edu\/\" target=\"_blank\" rel=\"noreferrer noopener\">MGB Communications<\/a><br \/><strong>Source: <\/strong><a href=\"https:\/\/news.harvard.edu\/\" target=\"_blank\" rel=\"noreferrer noopener\">Harvard<\/a><br \/><strong>Contact: <\/strong>MGB Communications \u2013 Harvard<br \/><strong>Image: <\/strong>The image is credited to Neuroscience News<\/p>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Original Research: <\/strong>Open access.<br \/>\u201c<a href=\"https:\/\/www.medrxiv.org\/content\/10.1101\/2024.04.13.24305771v2\" target=\"_blank\" rel=\"noreferrer noopener\">Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion<\/a>\u201d by Hossein Estiri et al. <em>MedRxiv<\/em><\/p>\n<hr class=\"wp-block-separator has-text-color has-pale-cyan-blue-color has-alpha-channel-opacity has-pale-cyan-blue-background-color has-background\"\/>\n<p><strong>Abstract<\/strong><\/p>\n<p><strong>Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion<\/strong><\/p>\n<p>Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9).<\/p>\n<p>In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts.<\/p>\n<p>The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm.<\/p>\n<p>Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code.<\/p>\n<p>Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code).<\/p>\n<p>Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC.<\/p>\n<p>The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients.<\/p>\n<p>The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID\u2019s genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.<\/p>\n<p> <!-- Form created by Optin Forms plugin by WPKube: create beautiful optin forms with ease! --> <!-- https:\/\/wpkube.com\/ --><!--optinforms-form5-container--> <!-- \/ Optin Forms --> <\/div>\n<p><script async src=\"https:\/\/pagead2.googlesyndication.com\/pagead\/js\/adsbygoogle.js?client=ca-pub-3711241968723425\"\r\n     crossorigin=\"anonymous\"><\/script>\r\n<ins class=\"adsbygoogle\"\r\n     style=\"display:block\"\r\n     data-ad-format=\"fluid\"\r\n     data-ad-layout-key=\"-fb+5w+4e-db+86\"\r\n     data-ad-client=\"ca-pub-3711241968723425\"\r\n     data-ad-slot=\"7910942971\"><\/ins>\r\n<script>\r\n     (adsbygoogle = window.adsbygoogle || []).push({});\r\n<\/script><br \/>\n<br \/><div data-type=\"_mgwidget\" data-widget-id=\"1660802\">\r\n<\/div>\r\n<script>(function(w,q){w[q]=w[q]||[];w[q].push([\"_mgc.load\"])})(window,\"_mgq\");\r\n<\/script>\r\n<br \/>\n<br \/><a href=\"https:\/\/neurosciencenews.com\/ai-long-covid-28003\/\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Summary: A new AI tool identified long COVID in 22.8% of patients, a much higher rate than previously diagnosed. By analyzing extensive health records from nearly 300,000 patients, the algorithm &hellip; <a href=\"https:\/\/hotvideos24.online\/?p=124038\" class=\"more-link\">Read More<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-124038","post","type-post","status-publish","format-standard","hentry","category-health","entry"],"_links":{"self":[{"href":"https:\/\/hotvideos24.online\/index.php?rest_route=\/wp\/v2\/posts\/124038","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hotvideos24.online\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hotvideos24.online\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hotvideos24.online\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/hotvideos24.online\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=124038"}],"version-history":[{"count":0,"href":"https:\/\/hotvideos24.online\/index.php?rest_route=\/wp\/v2\/posts\/124038\/revisions"}],"wp:attachment":[{"href":"https:\/\/hotvideos24.online\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=124038"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hotvideos24.online\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=124038"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hotvideos24.online\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=124038"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}