Augmented stream learning of medical time series data for continuous outcome prognosis

This project aims to develop novel stream learning algorithms for continuous patient outcome prognosis by taking into account patient’s data collected during ICU admission in a unified manner. The algorithms are expected to integrate high frequency time series data with patient’s demographicdata, lab data, diagnosis data, prescription data, etc. as exemplified in MIMIC-III, for accurate outcome prognosis. Issues such as prediction bias, data leakage, data sparsity, non-stationarity, model explainability will be investigated.