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	<title>Tri-Ensemble &#8211; BIOENGINEER.ORG</title>
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		<title>AI Ensemble Predicts Blood Glucose in Type 1 Diabetes with Minimal Data Cleanup</title>
		<link>https://bioengineer.org/ai-ensemble-predicts-blood-glucose-in-type-1-diabetes-with-minimal-data-cleanup/</link>
		
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		<pubDate>Wed, 07 Oct 2026 16:46:27 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[blood glucose prediction]]></category>
		<category><![CDATA[Clarke Error Grid]]></category>
		<category><![CDATA[Continuous Glucose Monitoring]]></category>
		<category><![CDATA[Explainable AI]]></category>
		<category><![CDATA[insulin-on-board]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Neural Computing and Applications]]></category>
		<category><![CDATA[OhioT1DM dataset]]></category>
		<category><![CDATA[patient-specific modeling]]></category>
		<category><![CDATA[SHAP]]></category>
		<category><![CDATA[Tri-Ensemble]]></category>
		<category><![CDATA[Type 1 diabetes]]></category>
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					<description><![CDATA[Researchers at Fayoum University show that a simple averaged ensemble of machine learning models, evaluated on the OhioT1DM dataset with minimal preprocessing and Clarke Error Grid analysis, delivers clinically competitive blood glucose forecasts for Type 1 Diabetes at 30- and 60-minute horizons, while exploratory SHAP analysis reveals patient-specific feature importance.]]></description>
		
		
		
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