<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>research Archives - A-Medicare</title>
	<atom:link href="https://a-medicare.com/tag/research/feed/" rel="self" type="application/rss+xml" />
	<link>https://a-medicare.com/tag/research/</link>
	<description>The Ultimate Destination for Healthcare</description>
	<lastBuildDate>Sun, 14 Aug 2022 22:53:27 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=6.9.4</generator>

<image>
	<url>https://a-medicare.com/wp-content/uploads/2022/01/PrefLogogold_512x512-150x150.png</url>
	<title>research Archives - A-Medicare</title>
	<link>https://a-medicare.com/tag/research/</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans</title>
		<link>https://a-medicare.com/common-pitfalls-and-recommendations-for-using-machine-learning-to-detect-and-prognosticate-for-covid-19-using-chest-radiographs-and-ct-scans/</link>
		
		<dc:creator><![CDATA[iCare]]></dc:creator>
		<pubDate>Sun, 13 Jun 2021 09:36:43 +0000</pubDate>
				<category><![CDATA[Industry Insight]]></category>
		<category><![CDATA[Research Considerations]]></category>
		<category><![CDATA[COVID]]></category>
		<category><![CDATA[research]]></category>
		<guid isPermaLink="false">https://www.a-medicare.org/?p=2298</guid>

					<description><![CDATA[<p>Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans Back Abstract Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been&#8230;</p>
<p>The post <a href="https://a-medicare.com/common-pitfalls-and-recommendations-for-using-machine-learning-to-detect-and-prognosticate-for-covid-19-using-chest-radiographs-and-ct-scans/">Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans</a> appeared first on <a href="https://a-medicare.com">A-Medicare</a>.</p>
]]></description>
		
		
		
			</item>
		<item>
		<title>Opening the Duke electronic health record to apps: Implementing SMART on FHIR</title>
		<link>https://a-medicare.com/opening-the-duke-electronic-health-record-to-apps-implementing-smart-on-fhir/</link>
		
		<dc:creator><![CDATA[iCare]]></dc:creator>
		<pubDate>Fri, 23 Apr 2021 03:28:14 +0000</pubDate>
				<category><![CDATA[Industry Insight]]></category>
		<category><![CDATA[Research Considerations]]></category>
		<category><![CDATA[research]]></category>
		<guid isPermaLink="false">https://www.a-medicare.org/?p=3166</guid>

					<description><![CDATA[<p>Back Opening the Duke electronic health record to apps: Implementing SMART on FHIR Highlights • The SMART on FHIR framework is a novel tool for EHR interoperability. • A custom integration of SMART on FHIR with the Epic EHR is demonstrated. • Several provider and patient apps are successfully integrated using this technique. • Security&#8230;</p>
<p>The post <a href="https://a-medicare.com/opening-the-duke-electronic-health-record-to-apps-implementing-smart-on-fhir/">Opening the Duke electronic health record to apps: Implementing SMART on FHIR</a> appeared first on <a href="https://a-medicare.com">A-Medicare</a>.</p>
]]></description>
		
		
		
			</item>
		<item>
		<title>Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal</title>
		<link>https://a-medicare.com/prediction-models-for-diagnosis-and-prognosis-of-covid-19-systematic-review-and-critical-appraisal/</link>
		
		<dc:creator><![CDATA[iCare]]></dc:creator>
		<pubDate>Tue, 12 Jan 2021 16:01:07 +0000</pubDate>
				<category><![CDATA[Industry Insight]]></category>
		<category><![CDATA[Research Considerations]]></category>
		<category><![CDATA[COVID]]></category>
		<category><![CDATA[research]]></category>
		<guid isPermaLink="false">https://www.a-medicare.org/?p=2301</guid>

					<description><![CDATA[<p>Back to homepage Abstract Final version accepted 12 January 2021 Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19&#8230;</p>
<p>The post <a href="https://a-medicare.com/prediction-models-for-diagnosis-and-prognosis-of-covid-19-systematic-review-and-critical-appraisal/">Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal</a> appeared first on <a href="https://a-medicare.com">A-Medicare</a>.</p>
]]></description>
		
		
		
			</item>
	</channel>
</rss>

<!--
Performance optimized by W3 Total Cache. Learn more: https://www.boldgrid.com/w3-total-cache/?utm_source=w3tc&utm_medium=footer_comment&utm_campaign=free_plugin

Object Caching 0/209 objects using Memcache
Page Caching using Disk: Enhanced 
Lazy Loading (feed)
Minified using Disk
Database Caching 1/123 queries in 0.066 seconds using Disk (Request-wide modification query)

Served from: a-medicare.com @ 2026-06-05 17:22:19 by W3 Total Cache
-->