  Life's most valuable asset is health. Continuously understanding the state of
our health and modeling how it evolves is essential if we wish to improve it.
Given the opportunity that people live with more data about their life today
than any other time in history, the challenge rests in interweaving this data
with the growing body of knowledge to compute and model the health state of an
individual continually. This dissertation presents an approach to build a
personal model and dynamically estimate the health state of an individual by
fusing multi-modal data and domain knowledge. The system is stitched together
from four essential abstraction elements: 1. the events in our life, 2. the
layers of our biological systems (from molecular to an organism), 3. the
functional utilities that arise from biological underpinnings, and 4. how we
interact with these utilities in the reality of daily life. Connecting these
four elements via graph network blocks forms the backbone by which we
instantiate a digital twin of an individual. Edges and nodes in this graph
structure are then regularly updated with learning techniques as data is
continuously digested. Experiments demonstrate the use of dense and
heterogeneous real-world data from a variety of personal and environmental
sensors to monitor individual cardiovascular health state. State estimation and
individual modeling is the fundamental basis to depart from disease-oriented
approaches to a total health continuum paradigm. Precision in predicting health
requires understanding state trajectory. By encasing this estimation within a
navigational approach, a systematic guidance framework can plan actions to
transition a current state towards a desired one. This work concludes by
presenting this framework of combining the health state and personal graph
model to perpetually plan and assist us in living life towards our goals.
