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	<title>HIRID &#8211; BIOENGINEER.ORG</title>
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		<title>Simple beats deep: linear interpolation outperforms AI models when real-world clinical data goes missing</title>
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				<category><![CDATA[Health]]></category>
		<category><![CDATA[autoencoders]]></category>
		<category><![CDATA[benchmarking]]></category>
		<category><![CDATA[clinical time series]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[electronic health records]]></category>
		<category><![CDATA[HIRID]]></category>
		<category><![CDATA[linear interpolation]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Markov chains]]></category>
		<category><![CDATA[MIMIC]]></category>
		<category><![CDATA[missing data imputation]]></category>
		<category><![CDATA[spatio-temporal missingness]]></category>
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					<description><![CDATA[A new benchmark study shows that simple linear imputation methods outperform deep learning models when realistic spatio-temporal missingness is simulated in clinical time series data.]]></description>
		
		
		
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