Predicting Patient Outcomes from a Few Hours of High Resolution Vital Signs Data

Monitoring of non-invasive, continuous, high-resolution patient vital signs (VS) such as heart rate and oxygen saturation is becoming increasingly common in hospital settings. These data are a potential boon for health informatics as a source of predictive information about a variety of patient outcomes. Yet the volume, noisiness, and per-patient idiosyncrasies of these data make their use extremely challenging. This paper explores the utility of representing VS data as unordered collections (bags) of local discrete patterns for the purpose of training classifiers to predict outcomes for traumatic brain injury patients, including mortality and level of cognitive function months after hospital discharge. The Symbolic Aggregate approXimation (SAX) algorithm is used for discretization, producing a bag of SAX "words" (local patterns) for each time series. Experiments with a dataset of sixty traumatic brain injury patients demonstrate that this approach is promising both in terms of predictive accuracy and patterns that it can reveal in the underlying VS data.