Real-time predictive modeling methods using multi-rate physiological time series data for the prediction of disease onset

点击翻译按钮获取中文摘要

The primary objective of this research program is to accelerate the development of state of the art predictive modeling methods in order to anticipate the onset of disease and other conditions. These methods will incorporate multiple multi-rate physiological time series data streams, including: fast rate data (typically measured at 250-1000 samples per second), such as electrocardiogram, the impedance respiratory waveform and intravenous blood pressure; slow rate data, such as blood oxygen saturation; and other clinical information. Current modeling methods are limited to simple analysis of these waveforms which precludes the consideration of their interactions except by clinicians themselves. The real-time predictive modeling methods to be used in the program combine system identification methods (such as Fast Orthogonal Search) with predictive methods such as those used in nonlinear model predictive control. Fast Orthogonal Search provides a means of effectively combining multi-rate data sources and determining parsimonious models. Initially, these methods will be applied in two contexts. The first is the neonatal intensive care unit where data streams are currently being collected in a collaborative pilot study involving UOIT, the Hospital for Sick Children in Toronto, and IBM's TJ Watson Research Laboratory, called the Artemis project. The second is home care through another collaboration with a care facility. The models and new methods resulting from the proposed research will be made available for use in clinical studies and implementation in the real world. There are potentially unlimited applications for these new real-time predictive modeling methods in areas where real-time event stream processing are critical, particularly where autonomous decision making and control will be enabled.