Economics Times<\/a><\/figcaption><\/figure>\n\n\n\n An artificial intelligence tool accurately predicted which patients newly infected with the COVID-19 virus would go on to develop severe respiratory disease, a new study found. <\/h2>\n\n\n\n
The work was led by NYU Grossman School of Medicine and the Courant \nInstitute of Mathematical Sciences at New York University, in \npartnership with Wenzhou Central Hospital and Cangnan People’s Hospital,\n both in Wenzhou, China.<\/p>\n\n\n\n
Named “SARS-CoV-2,” the new virus causes the disease called \n“coronavirus disease 2019” or “COVID-19.” As of March 30, the virus had \ninfected 735,560 patients worldwide. According to the World Health \nOrganization, the illness has caused more than 34,830 deaths to date, \nmore often among older patients with underlying health conditions. The \nNew York State Department of Health has reported more than 33,700 cases \nto date in New York City.<\/p>\n\n\n\n
Published online March 30 in the journal Computers, Materials & Continua<\/em>, the study also revealed the best indicators of future severity, and found that they were not as expected.<\/p>\n\n\n\n“While work remains to further validate our model, it holds promise \nas another tool to predict the patients most vulnerable to the virus, \nbut only in support of physicians’ hard-won clinical experience in \ntreating viral infections,” says corresponding study author Megan \nCoffee, MD, PhD, clinical assistant professor in the Division of \nInfectious Disease & Immunology within the Department of Medicine at\n NYU Grossman School of Medicine.<\/p>\n\n\n\n
“Our goal was to design and deploy a decision-support tool using AI \ncapabilities — mostly predictive analytics — to flag future clinical \ncoronavirus severity,” says co-author Anasse Bari, PhD, a clinical \nassistant professor in Computer Science at the Courant institute. “We \nhope that the tool, when fully developed, will be useful to physicians \nas they assess which moderately ill patients really need beds, and who \ncan safely go home, with hospital resources stretched thin.”<\/p>\n\n\n\n
Surprise Predictors <\/strong><\/p>\n\n\n\nFor the study, demographic, laboratory, and radiological findings \nwere collected from 53 patients as each tested positive in January 2020 \nfor the SARS-CoV2 virus at the two Chinese hospitals. Symptoms were \ntypically mild to begin with, including cough, fever, and stomach upset.\n In a minority of patients, however, severe symptoms developed with a \nweek, including pneumonia.<\/p>\n\n\n\n
The goal of the new study was to determine whether AI techniques \ncould help to accurately predict which patients with the virus would go \non to develop Acute Respiratory Distress Syndrome or ARDS, the fluid \nbuild-up in the lungs that can be fatal in the elderly.<\/p>\n\n\n\n
For the new study, the researchers designed computer models that make\n decisions based on the data fed into them, with programs getting \n“smarter” the more data they consider. Specifically, the current study \nused decision trees that track series of decisions between options, and \nthat model the potential consequences of choices at each step in a \npathway.<\/p>\n\n\n\n
The researchers were surprised to find that characteristics \nconsidered to be hallmarks of COVID-19, like certain patterns seen in \nlung images (e.g. ground glass opacities), fever, and strong immune \nresponses, were not useful in predicting which of the many patients with\n initial, mild symptoms would go to develop severe lung disease. Neither\n were age and gender helpful in predicting serious disease, although \npast studies had found men over 60 to be at higher risk.<\/p>\n\n\n\n
Instead, the new AI tool found that changes in three features — \nlevels of the liver enzyme alanine aminotransferase (ALT), reported \nmyalgia, and hemoglobin levels — were most accurately predictive of \nsubsequent, severe disease. Together with other factors, the team \nreported being able to predict risk of ARDS with up to 80 percent \naccuracy.<\/p>\n\n\n\n
ALT levels — which rise dramatically as diseases like hepatitis \ndamage the liver — were only a bit higher in patients with COVID-19, \nresearchers say, but still featured prominently in prediction of \nseverity. In addition, deep muscle aches (myalgia) were also more \ncommonplace, and have been linked by past research to higher general \ninflammation in the body.<\/p>\n\n\n\n
Lastly, higher levels of hemoglobin, the iron-containing protein that\n enables blood cells to carry oxygen to bodily tissues, were also linked\n to later respiratory distress. Could this explained by other factors, \nlike unreported smoking of tobacco, which has long been linked to \nincreased hemoglobin levels? Of the 33 patients at Wenzhou Central \nHospital interviewed on smoking status, the two who reported having \nsmoked, also reported that they had quit.<\/p>\n\n\n\n
Limitations of the study, say the authors, included the relatively \nsmall data set and the limited clinical severity of disease in the \npopulation studied. The latter may be due in part to an as yet \nunexplained dearth of elderly patients admitted into the hospitals \nduring the study period. The average patient age was 43.<\/p>\n\n\n\n
“I will be paying more attention in my clinical practice to our data \npoints, watching patients closer if they for instance complain of severe\n myalgia,” adds Coffee. “It’s exciting to be able to share data with the\n field in real time when it can be useful. In all past epidemics, \njournal papers only published well after the infections had waned.”<\/p>\n","protected":false},"excerpt":{"rendered":"
Date: March 30, 2020 Source 1: NYU Langone Health \/ NYU School of Medicine Source 2: www.sciencedaily.com Summary: An artificial intelligence tool accurately predicted which patients newly infected with the COVID-19 virus would go on to develop severe respiratory disease, a new study found. An artificial intelligence tool accurately predicted which patients newly infected with […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[29,32,30,31,33,34],"class_list":["post-684","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-ai","tag-computers","tag-cov-2","tag-covid-19","tag-journals","tag-treatment"],"aioseo_notices":[],"yoast_head":"\n
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