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	<title>machine learning healthcare &#8211; BIOENGINEER.ORG</title>
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		<title>FREMML: New Tool for Predicting Fracture Risk</title>
		<link>https://bioengineer.org/fremml-new-tool-for-predicting-fracture-risk/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sun, 25 Jan 2026 01:42:44 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[decision-support systems**]]></category>
		<category><![CDATA[fracture risk prediction]]></category>
		<category><![CDATA[FREMML]]></category>
		<category><![CDATA[İşte 5 uygun etiket: **Fracture risk prediction]]></category>
		<category><![CDATA[machine learning healthcare]]></category>
		<category><![CDATA[Machine Learning in Healthcare]]></category>
		<category><![CDATA[Makale içeriğine ve anahtar kelimelere göre en uygun 5 etiket: **FREMML]]></category>
		<category><![CDATA[osteoporosis management]]></category>
		<category><![CDATA[predictive analytics medicine**]]></category>
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					<description><![CDATA[A groundbreaking study published in the journal Archives of Osteoporosis has introduced an innovative approach named FREMML, aimed at revolutionizing how healthcare providers identify individuals at imminent risk of fractures. This new decision-support system leverages advanced machine learning techniques, integrating multiple sources of patient data to forecast fracture risk with unprecedented accuracy. As populations age [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">320533</post-id>	</item>
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		<title>Vietnam Study Uses AI for Toddler Autism Screening</title>
		<link>https://bioengineer.org/vietnam-study-uses-ai-for-toddler-autism-screening/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sat, 24 Jan 2026 02:25:44 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[AI autism screening]]></category>
		<category><![CDATA[İşte bu yazı için uygun 5 etiket (virgülle ayrılmış): **AI autism screening]]></category>
		<category><![CDATA[M-CHAT-R adaptation]]></category>
		<category><![CDATA[M-CHAT-R Vietnam]]></category>
		<category><![CDATA[machine learning healthcare]]></category>
		<category><![CDATA[perinatal predictors]]></category>
		<category><![CDATA[perinatal risk factors]]></category>
		<category><![CDATA[toddler risk stratification]]></category>
		<category><![CDATA[Vietnam autism study** **Kısa Açıklama:** 1. **AI autism screening:** Yapay zekanın otizm taramasında kullanımını doğrudan belirtir (makalenin temel konusu). 2]]></category>
		<category><![CDATA[Vietnam toddler study]]></category>
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					<description><![CDATA[In a groundbreaking study conducted in Vietnam, researchers have harnessed the power of machine learning to enhance autism risk stratification among toddlers. The study meticulously implemented the modified Checklist for Autism in Toddlers, Revised (M-CHAT-R), alongside various perinatal predictors, to establish a more nuanced understanding of autism risk factors in early childhood. Utilizing machine learning [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">320119</post-id>	</item>
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		<title>AI Forecasts Dental Complications and Follow-Up Needs</title>
		<link>https://bioengineer.org/ai-forecasts-dental-complications-and-follow-up-needs/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 20 Jan 2026 12:05:42 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[AI for Dental Complications]]></category>
		<category><![CDATA[AI in dentistry]]></category>
		<category><![CDATA[Dental extraction complications]]></category>
		<category><![CDATA[Dental Extraction Outcomes]]></category>
		<category><![CDATA[Follow-up Optimization]]></category>
		<category><![CDATA[İşte bu içerik için uygun 5 etiket: **Machine Learning Predictive Models]]></category>
		<category><![CDATA[machine learning healthcare]]></category>
		<category><![CDATA[Patient follow-up optimization]]></category>
		<category><![CDATA[predictive analytics]]></category>
		<category><![CDATA[Predictive Analytics in Healthcare** **Kısa Açıklama:** 1. **Machine Learning Predictive Models:** Araştırmanın temel metodolojisini (makine öğrenimi) ve amacını (tahmin) doğrudan vurgular. 2.]]></category>
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					<description><![CDATA[In a groundbreaking study published in Discov Artif Intell, researchers have introduced a novel machine learning approach to predict patient outcomes following dental extractions. Dental extractions are common procedures that can lead to various complications, including infections and excessive pain. Historically, predicting which patients might experience complications has been challenging, often leading to unnecessary follow-up [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">318892</post-id>	</item>
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		<title>Remote Real-Time Monitoring Revolutionizes Parkinson’s Care</title>
		<link>https://bioengineer.org/remote-real-time-monitoring-revolutionizes-parkinsons-care/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 12 Aug 2025 15:09:55 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[data-driven neurology]]></category>
		<category><![CDATA[machine learning healthcare]]></category>
		<category><![CDATA[Parkinson’s disease management]]></category>
		<category><![CDATA[real-time remote monitoring]]></category>
		<category><![CDATA[wearable sensor technology]]></category>
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					<description><![CDATA[In a groundbreaking advancement that promises to revolutionize the management of Parkinson’s disease, researchers have pioneered a remote real-time digital monitoring system that fills a long-standing clinical void. This technology offers unprecedented accuracy and timeliness in tracking the complex motor symptoms inherent in Parkinson’s, marking a critical leap forward in personalized patient care. Until now, [&#8230;]]]></description>
		
		
		
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