Towards precision health by enabling multimodal monitoring in real-life settings using uncertainty based hierarchical and time-dynamic models

01 November 2020 → 31 October 2021
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Data mining
    • Knowledge representation and reasoning
    • Machine learning and decision making
    • Health informatics
  • Medical and health sciences
    • Mental healthcare services
Interpretable machine learning mental health-care medical informatics multimodal data fusion confident machine learning
Project description

I will construct a multimodal and dynamic hierarchical sensing framework to tackle the challenges of personalized health monitoring in real-life settings. Multimodal sensing allows me to detect non-physiological symptoms by incorporating context. By fusing behavior modeling with hierarchical anomaly detection using an active learning approach, I will define the optimal moment to gather user feedback for the time dynamic models.