Sensing & Predictive Analytics for Computation Health System- CIE 212

SPACHeS Lab' research interests encompass health informatics with a focus on connected healthcare systems and prognostic analytics to address the current challenges of the P4 (Personalize, Preventive, Predictive, and Participative) medicine. Interdisciplinary research directions are:

1) Data-driven and Sensor-based Modeling to characterize the coupling dynamics of the pathological processes via investigating the nonlinear lump parameter model of the biological processes driven by the collected sensor data;

2)Medical Device Manufacturing and Bio-signal Processing for deploying customized signals and data by considering the wearable, non-invasive, IoT, and point-of-cared designs with the integration of nonlinear bio-signal processing techniques and machine learning algorithms.

3)Predictive Analyticsfor Personalized Healthcare to forecast acute event onsets by qualifying the transition of the system dynamics from the normal to abnormal conditions via time series prediction models integrated with nonlinear dynamic system approaches.

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