  Vital signs including heart rate, respiratory rate, body temperature and
blood pressure, are critical in the clinical decision making process. Effective
early prediction of vital signs help to alert medical practitioner ahead of
time and may prevent adverse health outcomes. In this paper, we suggest a new
approach called generative boosting, in order to effectively perform early
prediction of vital signs. Generative boosting consists of a generative model,
to generate synthetic data for next few time steps, and several predictive
models, to directly make long-range predictions based on observed and generated
data. We explore generative boosting via long short-term memory (LSTM) for both
the predictive and generative models, leading to a scheme called generative
LSTM (GLSTM). Our experiments indicate that GLSTM outperforms a diverse range
of strong benchmark models, with and without generative boosting. Finally, we
use a mutual information based clustering algorithm to select a more
representative dataset to train the generative model of GLSTM. This
significantly improves the long-range predictive performance of high variation
vital signs such as heart rate and systolic blood pressure.
