Panel Discussion: Big Data; Holly Jimison, PhD

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Tuesday, October 23, 2012
Panel Discussion: Big Data

Moderator: Roozbeh Jafari, PhD – Electrical Engineering, UT Dallas
Panelists: Holly Jimison, PhD – Medical Informatics & Clinical Epidemiology, OHSU James McClain, PhD – Physical Activity Epidemiologist , Risk Factor Monitoring & Methods Branch, National Cancer Institute (NCI) Lucila Ohno-Machado, MD, PhD – Associate Dean for Informatics & Technology, School of Medicine; Founding Chief, Division of Biomedical Informatics; Professor of Medicine, UC San Diego

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  • Behavioral Assessment & Intervention Commons 12/09/12 OHSU 6-month review In order to get a complete health state of a particular individual, especially elders and those with chronic diseases, it will be necessary to integrate mobile sensing with static data collection and intervention at home. This slide is a diagram, borrowed from the Oregon Center for Aging and Technology (ORCATECH) illustrating multimodal data monitoring system at home including medication adherence, sleep and interaction with computers (emails, computer games, etc.). Computers are also used in addition to the mobile telephones to mediate social interactions and to deliver coaching and rehabilitation.
  • This is an example of the kind of data we can obtain (which cannot be obtained with conventional research methods).
  • Panel Discussion: Big Data; Holly Jimison, PhD

    1. 1. Big Data Opportunities and Challenges in MonitoringHealth Behaviors in the Home and Environment Holly Jimison, PhD, FACMI Medical Informatics, Oregon Health & Science University Technology Advisor IPA, Office of Behavioral and Social Science Research, NIH
    2. 2. Home health based on unobtrusive, continuous monitoringBehavioral Markers = Continuous Monitoring + Computational Models
    3. 3. Activity Monitoring in the Home Sensor Events Private Home Bedroom Bathroom Living Rm Front Door Kitchen Hayes, ORCATECH 2007
    4. 4. Activity Monitoring in the Home Sensor Events Residential Facility Bedroom Bathroom Living Rm Front Door Kitchen Hayes, ORCATECH 2007
    5. 5. Measuring Gait in the Home• Unobtrusive gait measurement in-home with passive infrared (PIR) sensors - Hagler, et al., IEEE Trans Biomed Eng, 2010 – Four restricted view PIR sensors – Measure gait velocity whenever a subjects passes through the “sensor-line” – Deployed for the Intelligent Systems for Assessing Aging Changes (ISAAC) study – 200+ subjects monitored for up to 4 years and counting 5
    6. 6. Subject 1 0.035 90 Stroke 0.03 80 0.025Velocity (cm/s) 70 0.02 60 0.015 50 0.01 40 0.005 30 12/07 08/08 11/09 12/10 Time Austin et al, Sept 2011 - EMBC (Gait) 6
    7. 7. Subject 2 0.05 CDR=0.5 90 and MCI 0.045 diagnosis 0.04 80 0.035 Velocity (cm/s) 0.03 70 0.025 0.02 60 0.015 0.01 50 0.005 07/07 02/09 09/10 TimeAustin et al, Sept 2011 - EMBC (Gait) 7
    8. 8. Monitoring->Care Training Coaching GPS Decision Support EEG Chronic Care Social Networks Population Pulmonary SpO2 Function Statistics Health Information Epidemiology EvidencePosture ECG Blood Gait Pressure Inference Datamining Step Balance Height Performance Step Size Prediction Early DetectionM Pavel, H Watclar, Ref 8
    9. 9. ChallengesBig Data Challenges with Behavior Monitoring• Need low cost sensors / intelligent algorithms• Frequent data, but noisy and context dependent• Models of sensors, noise, context• Data harmonization• New modeling techniques – • Robust estimation and classification framework • Need advances in machine learning, data mining, fusion algorithms, modeling and visualization• Information fusion from multiple sources• Need dynamic user models, just-in-time feedback• Privacy / security advances• Address alert fatigue - containment of false alarms
    10. 10. Big Data Skill Sets• Sensor characterization (accuracy, bias, drift sampling rate, setting, etc.)• Intelligent data sampling• Data cleaning / missing data / understanding• Data visualization techniques, data representation• Data storage / transfer• Privacy / security of data• Modeling techniques• Analysis methods, sensor fusion
    11. 11. Big Data Skill Sets• Sensor characterization (accuracy, bias, drift sampling rate, setting, etc.)• Intelligent data sampling• Data cleaning / missing data / understanding• Data visualization techniques, data representation• Data storage / transfer• Privacy / security of data• Modeling techniques• Analysis methods, sensor fusion• Clinical or health relevance• Managing multidisciplinary teams, IRB, etc. NIH OBSSR Big Data Training: holly.jimison@nih.gov

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