personalizing medical treatments based on ambient information: towards interoperable monitoring...
DESCRIPTION
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.TRANSCRIPT
Personalizing Medical Treatments based on Ambient
Information
Towards Interoperable
Monitoring Applications
Rémi Bastide
ISIS – IRIT, France
http://www.irit.fr/~Remi.Bastide
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 !
3
Sources of Medical Information
• X-omics
• Electronic Health Records
• Medical Reimbursement History
• Social Media
Sensors and bio-Sensors
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
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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
6
Capturing biomedical data
7
Connected Health Devices
8
Connected Health Devices
• Monitor activity,
calories burnt,
heart rate,
sleeping…
9
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
13
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
15
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
19
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