UCLA

Remote Health Monitoring Systems (RHMS) provide a continuous stream of patient physiological data that allows nurses and doctors to make timely decisions and help patients manage their chronic conditions. As the number of patients effectively managed by these informational systems increases, the need for more sophisticated approaches in health analytics becomes significant.

The purpose of this work is to provide methodologies and tools for a more systematic approach in building RHMS. New algorithms are presented that mine various informational sources, especially Electronic Health Records (EHR), to design more generalizable analytics models for RHMS. In addition, a prototype RHMS system is developed that collects and graphically represents health data from a variety of sources.