volume 21, issue 4, P353-354 2006
DOI: 10.1016/j.jcrc.2006.10.018
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Abstract: Background: An average ICU patient is estimated to be described by more than 200 different parameters, making it likely that there is more information in this data than what is currently being extracted from it by humans. Machine learning methods could assist clinicians by analysing this large amount of ICU data to build models that predict the occurrence of specific clinical problems earlier than an experienced intesivist would.Purpose: To evaluate the applicability of machine learning methods for predicting …

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