Personalizing Medical Treatments based on Ambient
Information
Towards Interoperable
Monitoring Applications
Rémi Bastide
ISIS – IRIT, France
http://www.irit.fr/~Remi.Bastide
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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 !
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Sources of Medical Information
• X-omics
• Electronic Health Records
• Medical Reimbursement History
• Social Media
Sensors and bio-Sensors
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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
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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
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Capturing biomedical data
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Connected Health Devices
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Connected Health Devices
• Monitor activity,
calories burnt,
heart rate,
sleeping…
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Continous Sensing of bio-signals
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Smart clothing
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Smart Toilets
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Implanted or Ingestible Sensors
Fraunhofer Intravascular Monitoring System : placed in the femoral artery, measures blood pressure 30 times /s
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Monitoring medication adherence
Feasibility of an Ingestible Sensor-Based System for Monitoring Adherence to Tuberculosis Therapy,Belknap et al. 2012
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Lab-on-a-Chip
Nano-Tera project, EPFL, Switzerland
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Ambient sensors in smart housing
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Motion Sensing
• Computer vision (e.g. kinect, LeapMotion…)
• “X-ray” vision using wireless (wifi) signals
– Monitoring Breathing via Signal Strength in
Wireless Networks (Patwari et al. 2011)
– Wisee system
• Indoor location systems, RFID tags, sensors
in soles, accelerometer and gyroscope…
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Smart Meters
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LifeLogging
• The technical ability to
record and store every event
and information about one’s life
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From sensors to long-term monitoring
Low-level sensor events
• Light switches• RFIDs,• Contact sensors• Smart meters• …
Detection of Daily Life Activities
• Eating• Sleeping• Exercising• Toilet use• …
Deviation from
life habits over long term
• Nutrition disorders• Sleep disorders• …
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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”
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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
2003 Heat Wave : 15 000 over-
mortality in France, about 70 000 in
Europe
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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
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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 »
Publisher
Subscriber
Subscriber
« Provider », « Consumer » and « Transformer » components
• 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
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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
Dat
a Co
mm
unic
ation
Bus
XML
Emulation scenario
Scheduler Component
data
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Consumers• Consumers are components that are only using the data
transmitted on the communication bus
– Logger: Store the data exchanged on the communication
– 3D Visualization Component
Dat
a Co
mm
unic
ation
Bus XML
Emulation scenario
Logger Component data
Database
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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
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Semantic Interoperability : Semantic Sensor Networks
• Using and extending the Semantic
Sensor Network ontology developed
by the W3C
– Data exchanged between producers and
consumers is expressed in terms of this
ontology (« observation » concept)
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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…
Personalizing Medical Treatments based on Ambient
Information
Towards Interoperable
Monitoring Applications
Rémi Bastide
ISIS – IRIT, France
http://www.irit.fr/~Remi.Bastide
31
