Widespread chronic diseases (e.g., heart diseases, diabetes and its complications,
stroke, cancer, brain diseases) constitute a significant cause of rising healthcare
costs and pose a significant burden on quality-of-life for many individuals. Despite
the increased need for smart healthcare sensing systems that monitor / measure
patients’ body balance, there is no coherent theory that facilitates the modeling of
human physiological processes and the design and optimization of future healthcare
cyber-physical systems (HCPS). The HCPS are expected to mine the patient’s
physiological state based on available continuous sensing, quantify risk indices
corresponding to the onset of abnormality, signal the need for critical medical
intervention in real-time by communicating patient’s medical information via a
network from individual to hospital, and most importantly control (actuate) vital
health signals (e.g., cardiac pacing, insulin level, blood pressure) within personalized
homeostasis.
To prevent health complications, maintain good health and/or avoid fatal conditions
calls for a cross-disciplinary approach to HCPS design where recent statistical-physics
inspired discoveries done by collaborations between physicists and physicians are
shared and enriched by applied mathematicians, control theorists and bioengineers.
This critical and urgent multi-disciplinary approach has to unify the current state of
knowledge and address the following fundamental challenges: One fundamental
challenge is represented by the need to mine and understand the complexity of
the structure and dynamics of the physiological systems in healthy homeostasis
and associated with a disease (such as diabetes). Along the same lines, we need
rigorous mathematical techniques for identifying the interactions between integrated
physiologic systems and understanding their role within the overall networking
architecture of healthy dynamics. Another fundamental challenge calls for a deeper
understanding of stochastic feedback and variability in biological systems and
physiological processes, in particular, and for deciphering their implications not
only on how to mathematically characterize homeostasis, but also on defining new
control strategies that are accounting for intra- and inter-patient specificity – a truly
mathematical approach to personalized medicine.
Numerous recent studies have demonstrated that heart rate variability, blood glucose,
neural signals and other interdependent physiological processes demonstrate
fractal and non-stationary characteristics. Exploiting statistical physics concepts,
numerous recent research studies demonstrated that healthy human physiological
processes exhibit complex critical phenomena with deep implications for how homeostasis should be defined and how control strategies should be developed
when prolonged abnormal deviations are observed. In addition, several efforts have
tried to connect these fractal characteristics with new optimal control strategies that
implemented in medical devices such as pacemakers and artificial pancreas could
improve the efficiency of medical therapies and the quality-of-life of patients but
neglecting the overall networking architecture of human physiology. Consequently,
rigorously analyzing the complexity and dynamics of physiological processes
(e.g., blood glucose and its associated implications and interdependencies with
other physiological processes) represents a fundamental step towards providing
a quantifiable (mathematical) definition of homeostasis in the context of critical
phenomena, understanding the onset of chronic diseases, predicting deviations
from healthy homeostasis and developing new more efficient medical therapies that
carefully account for the physiological complexity, intra- and inter-patient variability,
rather than ignoring it.
This Research Topic aims to open a synergetic and timely effort between physicians,
physicists, applied mathematicians, signal processing, bioengineering and biomedical
experts to organize the state of knowledge in mining the complexity of physiological
systems and their implications for constructing more accurate mathematical models
and designing QoL-aware control strategies implemented in the new generation
of HCPS devices. By bringing together multi-disciplinary researchers seeking to
understand the many aspects of human physiology and its complexity, we aim
at enabling a paradigm shift in designing future medical devices that translates
mathematical characteristics in predictable mathematical models quantifying not
only the degree of homeostasis, but also providing fundamentally new control
strategies within the personalized medicine era.