Personalizing
MedicalTreatments
based on Ambient Information
Towards Interoperable
Monitoring Applications
Rémi Bastide
IS...
Big Data for Predictive and
Personalized Medicine
• Data mining : finding useful information from
vast data repositories
–...
Sources of Medical Information
• X-omics
• Electronic Health Records
• Medical Reimbursement History
• Social Media
Senso...
Outline of the talk
• Introduction (done)
• State of the art in ambient monitoring
– Monitoring bio-signals
– Monitoring a...
Ambient Data for Predictive and
Personalized Medicine
• Ambient Data is collected continuously,
unobtrusively, without dir...
Capturing biomedical data
6
Connected Health Devices
7
Connected Health Devices
• Monitor activity,
calories burnt, heart
rate, sleeping… 8
Continous Sensing of bio-signals
9
Smart clothing
10
SmartToilets
11
Implanted or Ingestible Sensors
12
Fraunhofer Intravascular Monitoring System : placed in the femoral artery,
measures blo...
Monitoring medication adherence
13
Feasibility of an Ingestible Sensor-Based System for Monitoring Adherence toTuberculosi...
Lab-on-a-Chip
14Nano-Tera project, EPFL, Switzerland
Ambient sensors in smart housing
15
Motion Sensing
• Computer vision (e.g. kinect, LeapMotion…)
• “X-ray” vision using wireless (wifi) signals
– Monitoring Br...
Smart Meters
17
LifeLogging
• The technical ability to
record and store every event
and information about one’s life
18
From sensors to long-term
monitoring
19
Techniques for inferring ADLs
from sensed data
• Machine-learning techniques
– Pre-training a computer system with benchma...
From clinical studies to
personalized home-care
• Many of the tools and techniques
presented above are currently
experimen...
Software engineering principles
• Weak coupling
– Construct software
applications as
assemblies of
components that are as
...
Weak coupling : publish /
subscribe architecture
• Components do not know each
other, nor speak directly to each
other
• I...
• Provide data to the communication bus
• Sensor components
– Act as proxies for hardware sensors
• Motion sensors
• Intel...
Providers
– Scheduler
• Simulate the activity of the user and feed simulated
data to the bus
• Useful for “benchmarking” a...
Consumers
• Consumers are components that are only using the data transmitted on
the communication bus
– Logger: Store the...
Transformers
• Transformers act both as
consumers and producers
– Based on Machine Learning or
Complex Event Processing
– ...
Semantic Interoperability :
Semantic Sensor Networks
28
• Using and extending the Semantic Sensor
Network ontology develop...
Towards Big-Data-Driven
Predictive Medicine
– Technology Providers What is possible ?
• or will become possible in the ne...
Personalizing
MedicalTreatments
based on Ambient Information
Towards Interoperable
Monitoring Applications
Rémi Bastide
IS...
31
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Personalizing medical treatments based on ambient information: towards interoperable monitoring applications

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Slides for my invited talk at UPCP'2013, the second Up Close and Personalized Congress.
Paris 25-28 July 2013, Paris, France

http://www.upcp.org/

Big Data refers to the new technical ability to digitally record, transmit and process massive amounts of digital data. Data mining technologies offer the possibility to extract meaningful knowledge from this data, through the analysis of statistical correlations.
Medicine has recently entered the realms of Personalization and Prediction: treatments become personalized to fit the patient's profile, and Prediction allows forecasting the likeliness of future health condition. Personalization and Prediction are based on patients and statistical medical data, coming from various sources: Electronic Health Records, Historical records of healthcare reimbursement, Genomics, Social media, Sensors and biosensors

Research and Industry are fueling a constant flow of innovation in this last field: Connected Health devices (including monitoring of Activities of Daily Life), smart clothing, implanted or ingestible sensors are increasingly being used to gather information about the patient’s health status or life habits. This innovation provides new sources of data essential to Personalized Medicine. In particular, this offers a brand new opportunity to correlate information gathered by these new sensors with the clinical information that is commonly gathered in clinical trials. For instance it is quite realistic to imagine a clinical trial performed at the patient’s home, where drug taking is precisely monitored by sensors in ingestible pills, while the drug’s clinical effects are correlated with constant monitoring of medical indicators such as blood pressure or heart rate, as well as with the performance of daily life activities such as eating, exercising, resting, sleeping, toilet use... This opens a new realm of opportunities in the design and analysis of clinical trials.

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Personalizing medical treatments based on ambient information: towards interoperable monitoring applications

  1. 1. Personalizing MedicalTreatments based on Ambient Information Towards Interoperable Monitoring Applications Rémi Bastide ISIS – IRIT, France Remi.Bastide@irit.fr http://www.irit.fr/~Remi.Bastide
  2. 2. Big Data for Predictive and Personalized Medicine • Data mining : finding useful information from vast data repositories – Combination of statistical and computational approaches – Finding unexpected correlations from seemingly unrelated data • Correlation is not causation ! 2
  3. 3. Sources of Medical Information • X-omics • Electronic Health Records • Medical Reimbursement History • Social Media Sensors and bio-Sensors 3
  4. 4. Outline of the talk • Introduction (done) • State of the art in ambient monitoring – Monitoring bio-signals – Monitoring activities of daily life • Problems • Technical Proposal – Software architecture – Semantic Interoperability 4
  5. 5. Ambient Data for Predictive and Personalized Medicine • Ambient Data is collected continuously, unobtrusively, without direct action from the user who continues performing his daily life activities as usual – Ambient biomedical data – Ambient behavioral data 5
  6. 6. Capturing biomedical data 6
  7. 7. Connected Health Devices 7
  8. 8. Connected Health Devices • Monitor activity, calories burnt, heart rate, sleeping… 8
  9. 9. Continous Sensing of bio-signals 9
  10. 10. Smart clothing 10
  11. 11. SmartToilets 11
  12. 12. Implanted or Ingestible Sensors 12 Fraunhofer Intravascular Monitoring System : placed in the femoral artery, measures blood pressure 30 times /s
  13. 13. Monitoring medication adherence 13 Feasibility of an Ingestible Sensor-Based System for Monitoring Adherence toTuberculosis Therapy, Belknap et al. 2012
  14. 14. Lab-on-a-Chip 14Nano-Tera project, EPFL, Switzerland
  15. 15. Ambient sensors in smart housing 15
  16. 16. Motion Sensing • Computer vision (e.g. kinect, LeapMotion…) • “X-ray” vision using wireless (wifi) signals – Monitoring Breathing via Signal Strength inWireless Networks (Patwari et al. 2011) – Wisee system • Indoor location systems, RFID tags, sensors in soles, accelerometer and gyroscope… 16
  17. 17. Smart Meters 17
  18. 18. LifeLogging • The technical ability to record and store every event and information about one’s life 18
  19. 19. From sensors to long-term monitoring 19
  20. 20. Techniques for inferring ADLs from sensed data • Machine-learning techniques – Pre-training a computer system with benchmark samples of the activity to be recognized • Model-based techniques (e.g. Complex Event Processing) – Pre-defining a computer model of the sequence of events that characterize the activity to be detected • The old fashioned way : clinical interviews and questionnaires – “Human as sensor” 20
  21. 21. From clinical studies to personalized home-care • Many of the tools and techniques presented above are currently experimented in clinical trials – Controlled cohorts and experimental setup – Ad-hoc software architecture – Usually targeted at a single pathology  Challenges in scaling up these results to the general population • Monitoring services for the elderly – Proportion of old people rising in the population – Developing chronic diseases, multi-pathology – Desire for home-care  Developing sustainable monitoring services, that can be tailored to the specific case of the patient 21 2003 HeatWave : 15 000 over-mortality in France, about 70 000 in Europe
  22. 22. Software engineering principles • Weak coupling – Construct software applications as assemblies of components that are as independent as possible to each other • Syntactic and Semantic Interoperability – Syntactic : all software components speak the same language – Semantic : the meaning of exchanged information is preserved 22
  23. 23. Weak coupling : publish / subscribe architecture • Components do not know each other, nor speak directly to each other • Instead components « publish » information about a designated « topic », or manifest their interest in a topic by « subscribing » to it – « Software bus » 23 Publisher Subscriber Subscriber « Provider », « Consumer » and «Transformer » components
  24. 24. • Provide data to the communication bus • Sensor components – Act as proxies for hardware sensors • Motion sensors • Intelligent pillow • Inertial navigation sensors carried on by the patient • Medical equipment • … – Translation from proprietary language to bus-compliant data Providers Sensor Component Hardware Sensors Data Communication Bus Proprietary Language
  25. 25. Providers – Scheduler • Simulate the activity of the user and feed simulated data to the bus • Useful for “benchmarking” and validating detection algorithms or systems – Based on simulation – Based on real-time captured data logged during previous experiments 25 DataCommunicationBus XML Emulation scenario Scheduler Component data
  26. 26. Consumers • Consumers are components that are only using the data transmitted on the communication bus – Logger: Store the data exchanged on the communication – 3DVisualization Component 26 DataCommunicationBus XML Emulation scenario Logger Component data Database
  27. 27. Transformers • Transformers act both as consumers and producers – Based on Machine Learning or Complex Event Processing – Simple transformers • only use data produced by regular producers – Advanced transformers • use data produced by producers and/or by other transformers • Simple transformers – Fall detection (e.g. from skin’s electrical resistance and heart rate [Noury 2013]) – Sleeping monitors – Activity monitor (e.g. smart meters + location sensors detects the act of preparing breakfast) • Advanced transformers – Denutrition detector : variations in the rate of preparing food + readings from a wireless scale 27
  28. 28. Semantic Interoperability : Semantic Sensor Networks 28 • Using and extending the Semantic Sensor Network ontology developed by theW3C – Data exchanged between producers and consumers is expressed in terms of this ontology (« observation » concept)
  29. 29. Towards Big-Data-Driven Predictive Medicine – Technology Providers What is possible ? • or will become possible in the next few years thanks to Moore’s law – Medicine Practitioners  What is useful ? • Sustainability, cost / benefit ratio for the Health system – Society at large What is ethical ? • Issues about data security, privacy, screening… 29
  30. 30. Personalizing MedicalTreatments based on Ambient Information Towards Interoperable Monitoring Applications Rémi Bastide ISIS – IRIT, France Remi.Bastide@irit.fr http://www.irit.fr/~Remi.Bastide
  31. 31. 31

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