Dr. Patricia Mabry Appointed Acting Deputy Director of OBSSR, NIH
May 8, 2013
2013 Mobile Health (mHealth) Summer Training Institute is now accepting applications
Deadline: May 24, 2013
"NIH Toolbox for assessment of neurological and behavioral function"
A supplement to Neurology
June 12, 2013
4 pm - 5 pm E.T.
NIH Adherence Network Distinguished Speaker Series:
Improving Patient-reported Assessments of Medication Adherence and Integrating them into Routine Clinical Care
June 14, 2013
2 pm - 3 pm E.T.
BSSR Lecture Series:
Scaling Up the Social Networks Revolution in Health
June 3 - 7, 2013
St. Louis, Missouri
Training Institute for Dissemination and Implementation Research in Health
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We are experiencing some technical difficulty with the webinar footage. We hope to get it resolved soon. In the meantime please find the slides below.
Deborah Estrin, Ph.D., Professor of Computer Science, Cornell NYC Tech
January 29, 2013
Presentation Slides (PDF 2.43MB)
The most significant health and wellness challenges increasingly involve multiple chronic conditions, from diabetes, hypertension, and asthma to depression, chronic-pain, sleep and neurological disorders. The promise of mobile health (mHealth) is that we can leverage the power and ubiquity of mobile and cloud technologies to monitor and understand symptoms, side effects and treatment outside the clinical setting, thereby closing the feedback loops of self-care, clinical-care, and personal-evidence-creation. However, to realize this promise, we must develop new data capture, processing and modeling techniques to convert the ‘digital exhaust’ emitted by mobile phone use into behavioral biomarkers. This calls for the sort of modular layered processing framework used in speech and vision in which low level state classifications of raw data (e.g., estimated activity states such as sitting, walking, driving from continuous accelerometer and location traces), are used to derive mid-level semantic features (e.g., total number of ambulatory minutes, number of hours spent out of house), that can then be mapped to particular behavioral biomarkers for specific diseases (e.g., chronic pain, GI dysfunction, MS, fatigue, depression, etc). The techniques needed to derive these markers will range from simple functions to machine learning classifiers, and will need to fuse diverse data types, but all will need to cope with noisy, erratic data sources.
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