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		<title>Machine learning with patient words sharpens emergency triage classification</title>
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		<pubDate>Sun, 25 Oct 2026 03:42:07 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[ADASYN]]></category>
		<category><![CDATA[Brazil]]></category>
		<category><![CDATA[emergency medicine]]></category>
		<category><![CDATA[Emergency Severity Index]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Natural Language Processing]]></category>
		<category><![CDATA[Patient Safety]]></category>
		<category><![CDATA[retrospective cohort study]]></category>
		<category><![CDATA[SMOTE]]></category>
		<category><![CDATA[TF-IDF]]></category>
		<category><![CDATA[triage]]></category>
		<category><![CDATA[XGBoost]]></category>
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					<description><![CDATA[A retrospective cohort study of 64,382 adult emergency department patients in Brazil found that adding natural language processing of chief complaints to machine learning classifiers improved triage classification under the Emergency Severity Index, with XGBoost reaching a ROC-AUC of 0.873 on test data.]]></description>
		
		
		
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