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

Long-range Prediction of Vital Signs Using Generative Boosting via LSTM Networks