Development of temporal context-based feature abstractions for enabling monitoring and managing of interventions

Disease self-management programs and intervention/care plan monitoring are often unable to systematically leverage patient-generated information, especially those requiring interpretation of the temporal contexts of the measurement. While existing techniques help in capturing and storing the relevant data, their ability to determine appropriate metrics most sensitive to that individual is limited or non-existent. This is attributable to the lack of unifying models for enabling such interpretations and the non-trivial process required to generate meaningful feature abstractions to support individualized prognosis. To address these issues, a data-driven approach designed to identify the right abstractions for key features relevant to personalization and monitoring of care is discussed.