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	<title>Automated Machine Learning &#8211; BIOENGINEER.ORG</title>
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		<title>Automated Machine Learning Enhances Frailty Index Comparisons</title>
		<link>https://bioengineer.org/automated-machine-learning-enhances-frailty-index-comparisons/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sat, 10 Jan 2026 11:53:28 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Automated Machine Learning]]></category>
		<category><![CDATA[Automated Machine Learning (AutoML)]]></category>
		<category><![CDATA[Frailty Assessment]]></category>
		<category><![CDATA[Healthcare Technology]]></category>
		<category><![CDATA[İşte içeriğe uygun 5 etiket: **Frailty Index Assessment]]></category>
		<category><![CDATA[patient outcomes]]></category>
		<category><![CDATA[predictive analytics in healthcare]]></category>
		<category><![CDATA[Spinal Surgery]]></category>
		<category><![CDATA[Spinal Surgery Outcomes]]></category>
		<category><![CDATA[Surgical Risk Stratification** **Kısa Açıklama:** 1. **Frailty Index Assessment:** İçeriğin temel konusu (kırılganlık indekslerinin değerlendirilmesi ve karşılaştırılması).]]></category>
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					<description><![CDATA[In the rapidly evolving world of medicine, the intersection of artificial intelligence and healthcare offers a promising frontier for improving patient outcomes, particularly in complex surgical fields such as spinal surgery. A recent study titled “Leveraging Automated Machine Learning to Benchmark, Deconstruct, and Compare Frailty Indices for Predicting Adverse Spinal Surgery Outcomes,” authored by Ghosh, [&#8230;]]]></description>
		
		
		
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