Panel at AMIA 2013 Conference on big data - The Exposome and the quantified self fjms

1,285 views
1,072 views

Published on

Presentation by Prof. Fernando MArtin-Sanchez, Director of the Health and Biomedical Informatics Centre (HaBIC) of the University of Melbourne at at the Panel on Big Data in Health and Biomedical Research, at the annual AMIA 2013 Conference, 19th November, Washington DC

Published in: Health & Medicine, Technology
1 Comment
1 Like
Statistics
Notes
  • Dear Fernando, it would be very nice if in your wonderful presentations include the concept of Health 4.0 since it is essential in the BigData handing. Herewith enclose the link and I will provide a slide if you want. Many kisses. http://lnkd.in/dtGMNnM http://lnkd.in/dQnEhpm
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
No Downloads
Views
Total views
1,285
On SlideShare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
61
Comments
1
Likes
1
Embeds 0
No embeds

No notes for slide

Panel at AMIA 2013 Conference on big data - The Exposome and the quantified self fjms

  1. 1. Panel: Biomedical and Healthcare Analytics on Big Data Self-Quantification Systems: Big Data Prospects and Challenges Fernando J. Martin-Sanchez Professor and Chair of Health Informatics Melbourne Medical School & Director, Health and Biomedical Informatics Centre (HABIC)
  2. 2. Why Self-Monitoring? BIG DATA Social media Quantified Self Participatory health Crowsourced Clinical trials Exposome
  3. 3. Participatory health I.  Personal genome services (BYO) II.  Personal diagnostic testing (BYO) III.  Personal medical image management (DIY) IV.  Personal sensing and monitoring (DIY) V.  Personal health records (DIY) VI.  Patient reading doctor’s notes VII.  Patient initiating clinical trials VIII.  Patient reporting outcomes IX.  Patient accessing health information X.  Shared decision making Collecting data Participatory health Exchanging information
  4. 4. Availability of new sensors for data collection Exposome Genome Phenome Environmental risk factors (pollution, radiation, toxic agents, …) Biomarkers (DNA sequence, Epigenetics) Anatomy, Physiological, biochemical parameters (cholesterol, temperature, glucose, heart rate…) Social media / Integrated personal health record / Personal Health Systems
  5. 5. Exposome informatics (JAMIA Oct 2013)
  6. 6. Quantified Self: The concept
  7. 7. Quantified Self: The community
  8. 8. New market Global annual wearable device unit shipments crossing the 100 million milestone in 2014, and reaching 300 million units five years from now Gartner hype cycle Corporate health plans – 13 Mill
  9. 9. The Quantified Self community •  Quantified Self is a collaboration of users and tool makers who share an interest in self knowledge through self-tracking. •  We exchange information about our personal projects, the tools we use, tips we’ve gleaned, lessons we’ve learned. We blog, meet face to face, and collaborate online. There are three main “branches” to our work. –  The Quantified Self blog and community site. –  Show and Tell meetings (Meetup groups) - Melbourne –  Quantified Self Conferences (US and Europe) •  Groups 112, Members 17,893, Cities 89, Countries 31
  10. 10. The IBES SELF-OMICS Project •  Addressing the information and communication needs of the ‘quantified individual’ for enabling participatory and personalised medicine •  Funded by IBES (Institute for a Broadband Enabled Society) - 2012-2013 •  Resources: http://www.broadband.unimelb.edu.au/health/monitoring/selfomics.html http://www.scoop.it/t/selfomics http://pinterest.com/hbir/self-omics-self-monitoring-quantified-self-omics/
  11. 11. Variety of self-monitoring devices, sensors and services MoodPanda Actipressure Zeo Sleep Manager Fitbit 23andMe Sensaris Senspod uBiome iBGStar 12
  12. 12. 13
  13. 13. QS Lab
  14. 14. White Paper http:// www.broadband.unimelb.edu.au
  15. 15. Classification of self-quantification systems •  Capture data directly from the user (Primary or Secondary) •  Sensor Location (Mobile or Fixed) •  Involve skin pricking (In-contact or On-body) •  Data type (Environmental or Touchless) •  Location of data integration (Software-based or Hardwarebased integration) •  Location of data visualisation 16 (Standalone, etc.)
  16. 16. Data flow stages in Zeo Sleep Manager Data Flow Stages Data Collecting Data Transmission Data Saving (temporary storage) Data Storing (permanent storage) Data Analysis Data Visualisation Zeo Sleep Manager Data Sharing 17
  17. 17. Second white paper – user guide
  18. 18. Second White Paper – Data integration methods PCEHR Individual analysis Integrated Analysis
  19. 19. Minimum Information about a Self Monitoring Experiment (MISME) EXPERIMENT Who/Which part/ Where/When? Body part (FMA) Sample Device How? Name Model Manufacturer Time Technical Specs Location Taxonomy What Measurement Body structure Body function Around body (based on WHO) Method Data Raw Where is it stored Procedures Processed Units
  20. 20. Self-Omics •  QS as an interface to the Human Body •  How much information? •  People-as-sensors •  Making the personal public •  From population surveillance to individual surveillance Infography: Institute for Health Technology Transformation
  21. 21. Benefits If 10% adults USA began a regular walking program, an estimated $5.6 Billion in heart disease could be saved.
  22. 22. Self-monitoring •  Project MUM-Size –  Study of very obese pregnant women – risk of complications due to anesthesia during labor –  Using fitbit and social media support by research midwives in the intervention group to prevent weight gain during pregnancy –  User guide (Aria scale not suitable for pregnant women, limit of 140 Kgs of weight)
  23. 23. Convergence between personalised and participatory medicine
  24. 24. Health Informatics aspects of QS •  Integration of QS data with EHR/ •  •  •  •  •  •  •  PHR HIE of 1 - Blue Button and Blue Button+ Meaningful use - V/D/T View/ Download/Transmit. Making sense of data Behaviour change From QS early adopter to mainstream motivated-self Long-term or too-scary does not work Personalization Designers or artists for data presentation
  25. 25. Conclusion Benefits Challenges •  Privacy •  Security •  Education of their health •  Cyberchondria •  Self-improvement •  Equity •  Risk profiling •  Regulation, accreditation •  Prevention •  Shift terciary à secondary •  Role of the clinician •  Infrastructure needs à primary à home care •  Therapeutic gap (ethics) •  Data donors for research •  Motivation •  Deepening understanding
  26. 26. References • Almalki, M, Martin-Sanchez, F & Gray, K 2013, 'SelfQuantification: The Informatics of Personal Data Management for Health and Fitness’, Institute for a Broadband-Enabled Society (IBES). • Almalki, M, Martin-Sanchez, F & Gray, K 2013. The Use of Self-Quantification Systems: Big Data Prospects and Challenges. Proceedings of HISA BIG DATA 2013 conference. Accepted for publication at BMC Health Information Science and Systems 29
  27. 27. UoM QS team •  Dr. Kathleen Gray (HaBIC) •  Manal Almalki (PhD candidate, HaBIC) •  Pilar Cantero (RA - HaBIC) •  Cecily Gibert (RA - HaBIC) •  Dr. Bernd Ploderer (Computing and Information Systems) •  Mark Whooley (MIS student) •  Matthew McGavern (MIS student) •  Prof. David Story (Chair of Anesthesia) •  Prof. Mary Wlodek (Physiology)
  28. 28. Thank you for your attention! © Copyright The University of Melbourne 2012

×