<?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>ATTR amyloidosis &#8211; BIOENGINEER.ORG</title>
	<atom:link href="https://bioengineer.org/tag/attr-amyloidosis/feed/" rel="self" type="application/rss+xml" />
	<link>https://bioengineer.org</link>
	<description>Bioengineering</description>
	<lastBuildDate>Sat, 10 Oct 2026 08:19:53 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=7.1.3</generator>

<image>
	<url>https://bioengineer.org/wp-content/uploads/2019/09/cropped-bioengineering-32x32.png</url>
	<title>ATTR amyloidosis &#8211; BIOENGINEER.ORG</title>
	<link>https://bioengineer.org</link>
	<width>32</width>
	<height>32</height>
</image> 
<site xmlns="com-wordpress:feed-additions:1">72741379</site>	<item>
		<title>Machine Learning Tool Mines Health Records to Catch Hidden Heart Disease</title>
		<link>https://bioengineer.org/machine-learning-tool-mines-health-records-to-catch-hidden-heart-disease/</link>
		
		<dc:creator><![CDATA[]]></dc:creator>
		<pubDate>Sat, 10 Oct 2026 08:19:53 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Technology]]></category>
		<category><![CDATA[ATTR amyloidosis]]></category>
		<category><![CDATA[bone scintigraphy]]></category>
		<category><![CDATA[cardiac amyloidosis]]></category>
		<category><![CDATA[Clinical decision support]]></category>
		<category><![CDATA[diagnostic delay]]></category>
		<category><![CDATA[electronic health records]]></category>
		<category><![CDATA[external validation]]></category>
		<category><![CDATA[heart failure]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[nuclear medicine]]></category>
		<category><![CDATA[PLOS Digital Health]]></category>
		<category><![CDATA[risk prediction]]></category>
		<guid isPermaLink="false">https://bioengineer.org/?p=412274</guid>

					<description><![CDATA[A machine learning model called Amylo-Detect uses routine electronic health record data to identify patients at risk for cardiac amyloidosis, outperforming existing scores and catching cases missed in clinical practice across two European hospitals.]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">412274</post-id>	</item>
	</channel>
</rss>
