Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers
This work addresses alarm fatigue in ICU nurses, but it is incremental as it compares existing methods without introducing new techniques.
The study tackled the problem of false alarms in ICU bedside monitors by comparing sliding window and recurrent predictors for classifying patient state-of-health from multivariate time series, finding that RNNs performed slightly better for three out of four targets.
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.