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Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers

Bedside monitors in Intensive Care Units (ICUs) frequently sound incorrectly, slowing response times and desensitising nurses to alarms (Chambrin, 2001), causing true alarms to be missed (Hug et al., 2011). We compare sliding window predictors with recurrent predictors to classify patient state-of-health from ICU multivariate time series; we report slightly improved performance for the RNN for three out of four targets.

preprint2016arXivOpen access
Adam McCarthyChristopher K. I. Williams
Machine Learning
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Open access2 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Adam McCarthyChristopher K. I. Williams

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