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

Abstract

NIPS 2016 Workshop on Machine Learning for Health

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.

Additional information

Adam McCarthy, Christopher K.I. Williams (2016)