Research Focus
Research Statement
SPACHeS Lab primary research interests encompass health informatics with a focus on connected healthcare systems and prognostic analytics to address the current challenges of the P4 ((Prediction – Prevention – Personalized – Participation) paradigm. SPACHeS Lab's interdisciplinary research directions include 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, and point-of-cared designs with the integration of nonlinear bio-signal processing techniques and machine learning algorithms and 3) Predictive Analytics for 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.
The research settings range widely, from in-vitro data (from simulated data) and in-vivo (from online databases) to human subject data. Hence, the remaining goals are: to build systems and methods utilizing bio-signals and data, subsequently to process and analyze collected data for a better understanding of the system dynamics related to disease diagnostics, and eventually to predict the onset, progression of, and recovery from pending deterioration, which can in turn be embedded in the systems to help doctors make better-informed decisions related to preventive treatment.
