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	<title>clinical decision support systems &#8211; BIOENGINEER.ORG</title>
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		<title>AI ECG Alerts Improve Potassium Imbalance Treatment</title>
		<link>https://bioengineer.org/ai-ecg-alerts-improve-potassium-imbalance-treatment/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Thu, 08 Jan 2026 14:15:35 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Acute Care Innovations]]></category>
		<category><![CDATA[artificial intelligence in cardiology]]></category>
		<category><![CDATA[clinical decision support systems]]></category>
		<category><![CDATA[Elektrolit Bozuklukları]]></category>
		<category><![CDATA[Gerçek Zamanlı Hasta İzleme]]></category>
		<category><![CDATA[İçeriğe uygun 5 etiket: **Yapay Zeka Destekli EKG]]></category>
		<category><![CDATA[Klinik Karar Destek Sistemleri** **Açıklama:** 1. **Yapay Zeka Destekli EKG:** Makalenin temel teknoloj]]></category>
		<category><![CDATA[Potassium Imbalance Detection]]></category>
		<category><![CDATA[Potasyum Dengesizliği Tedavisi]]></category>
		<category><![CDATA[Real-time ECG Monitoring]]></category>
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					<description><![CDATA[In recent years, artificial intelligence (AI) has profoundly transformed numerous fields of medicine, promising enhanced diagnostic accuracy and improved patient care. Now, a pioneering study published in Nature Communications by Lin, C., Lin, CS., Chen, SJ., and colleagues has advanced this revolution by developing an AI-enabled electrocardiogram (ECG) alert system tailored specifically to detect potassium [&#8230;]]]></description>
		
		
		
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		<title>Evaluating Physicians’ Use of Blood Management Decision Support</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 21 Nov 2025 16:19:57 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[blood management]]></category>
		<category><![CDATA[clinical decision support systems]]></category>
		<category><![CDATA[data-driven healthcare**]]></category>
		<category><![CDATA[physician experiences]]></category>
		<category><![CDATA[technology in healthcare]]></category>
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					<description><![CDATA[In a rapidly evolving healthcare landscape, the integration of technology into clinical practices is not just an option; it is becoming a necessity. The introduction of Clinical Decision Support Systems (CDSS) is one such technological advancement that has shown promise in improving patient outcomes, particularly within the domain of patient blood management. In a groundbreaking [&#8230;]]]></description>
		
		
		
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		<title>Machine Learning Differentiates Abdominal IgA Vasculitis, Appendicitis</title>
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		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Thu, 23 Oct 2025 08:16:05 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[AI-driven differential diagnosis]]></category>
		<category><![CDATA[clinical decision support systems]]></category>
		<category><![CDATA[IgA vasculitis vs appendicitis]]></category>
		<category><![CDATA[machine learning in pediatric diagnostics]]></category>
		<category><![CDATA[predictive analytics in medicine]]></category>
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					<description><![CDATA[In a groundbreaking development at the intersection of pediatric medicine and artificial intelligence, researchers Harijith and Pallavoor have unveiled a novel application of machine learning that promises to revolutionize the diagnosis of complex abdominal conditions in children. Their study, published in the prestigious journal Pediatric Research, introduces an innovative computational approach aimed at differentiating abdominal [&#8230;]]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">285683</post-id>	</item>
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		<title>Interpretable AI Predicts Toxicity in Cervical Cancer</title>
		<link>https://bioengineer.org/interpretable-ai-predicts-toxicity-in-cervical-cancer/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 06 Oct 2025 17:40:34 +0000</pubDate>
				<category><![CDATA[Cancer]]></category>
		<category><![CDATA[cervical cancer treatment]]></category>
		<category><![CDATA[clinical decision support systems]]></category>
		<category><![CDATA[hematologic toxicity prediction]]></category>
		<category><![CDATA[interpretable AI]]></category>
		<category><![CDATA[radiomics-dosimetrics integration]]></category>
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					<description><![CDATA[In a groundbreaking development poised to reshape oncological care, researchers have unveiled a sophisticated machine learning model capable of predicting hematologic toxicity (HT) in patients with advanced cervical cancer undergoing chemoradiotherapy. By harnessing the power of interpretable artificial intelligence and integrating multifaceted radiomic and dosimetric data, this novel approach promises to enhance clinical decision-making and [&#8230;]]]></description>
		
		
		
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