Personalizing Medical Treatments based on Ambient

Information

Towards Interoperable

Monitoring Applications

Rémi Bastide

ISIS – IRIT, France

Remi.Bastide@irit.fr

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

6

Capturing biomedical data

7

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

Remi.Bastide@irit.fr

http://www.irit.fr/~Remi.Bastide

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