We invite you to submit abstracts for our interdisciplinary meeting.
Computational Physiology aims to understand human health state from an
array of "noisy" sensors. The application of sophisticated state
estimation techniques to this domain could have a significant impact.
Details of the symposium follow:
COMPUTATIONAL PHYSIOLOGY SYMPOSIUM:
March 21-23, 2011. Stanford University.
Computational Physiology – SECOND CALL FOR ABSTRACTS (Supplemental
Website: http://sites.google.com/site/aaaicomputationalphysiology/home)
SUBMISSION DATES:
* Abstracts submissions (200 - 400 Words): November 19, 2010.
* Notification of acceptance: December 10, 2010.
* Extended Abstract (2 Pages) or Short Paper (4 - 6 Pages) Due:
January 21, 2011.
SPEAKERS:
* Dave Andre - (Body Media)
* Jeff Ashe, (GE Research), Environmental sensing: non-contact vital
signs.
* Matthew Goodwin - (MIT Media Lab, Director of Clinical Research, Co-
Director, Autism & Communication Technology)
* David Klonoff (Clinical Professor of Medicine, U.C. San Francisco,
Editor-in-Chief, Journal of Diabetes Science and Technology Medical
Director, Diabetes Research Institute) Key Note Talk: Smart sensors
for Maintaining Homestasis.
* Brent Ruby (University of Montana, Department of Health and Human
Performance) Wildland firefighters application area.
* Zeeshan Syed - (University of Michigan, Assistant Professor
University of Michigan, Department of Electrical Engineering and
Computer Science)
Automated human health-state monitoring aims to identify when an
individual moves from a healthy to a compromised state. For example,
changes in diet or physical activity can lead to life-threatening hypo
or hyperglycemia in diabetics. Similarly, elderly individuals managing
multiple chronic conditions may experience rapid changes in physical
and cognitive health state that must be caught quickly for treatments
to be most effective. Even in healthy individuals, heavy exertion in
extreme climates can quickly lead to life threatening situations.
The emergence of inexpensive and unobtrusive health sensors promises
to shift the health care industry‘s focus from episodic care in acute
settings to early detection and longitudinal care for chronic
conditions in natural living environments. While these sensing systems
are able to provide a wealth of physiological information, the non-
invasive measurements are often quite different from the high-quality
but limited quantities of data used by physicians. As the availability
of longitudinal data increases, we have an unprecedented opportunity
to discover new early predictors of clinically significant events.
This symposium will bring together researchers from the fields of
artificial intelligence, machine learning, engineering, physiology,
and medicine for a set of talks and discussions aimed at bridging
these inter-disciplinary perspectives. Researchers in all fields
related to computational physiology are invited to submit extended
abstracts (2 pages) or short papers (4-6 pages) describing:
* New ambulatory and non-contact sensing technologies or novel
applications of existing sensors
* Specific difficulties associated with measuring human health states
of interest (e.g. internal body temperature, hydration, cognitive
decline, blood glucose level).
* Inference techniques that address the challenges of decision-making
with these data (e.g. continuous monitoring, multi-sensor fusion,
movement artifacts).
* Interfaces/Approaches for providing real-time advice to individuals
towards preventing injury and maintaining health.
Reports on experimental results, descriptions of implemented systems,
and position papers are all welcome; papers will be chosen for either
oral or poster presentations.
Extended Abstracts and Short Papers should be in AAAI format [http://
www.aaai.org/Publications/Author/author.php]. Please e-mail
submissions to mbuller at cs.brown.edu.
ORGANIZING COMMITTEE
* Mark Buller, (Brown University, mbuller at cs.brown.edu)
* Paul Cuddihy (GE Research, cuddihy at ge.com)
* Finale Doshi-Velez (Massachusetts Institute of Technology, finale at
mit.edu)
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