Hamid Mcheick. Internet of Things healthcare system for reducing economic burden. (ICDEc 2021)

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Section I: Introduction
 Motivation
 Statistics
Section II : Problem
 Problematic
 Goals
Section III: Monitoring system & validation
 Pervasive healthcare systems
 Ontology, Medical rules
 Experimentation
 Extended version of this system
Section IV: Conclusion
 Contribution
 Perspectives
?
Outline

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 250 million people suffer from COPD globally [1]
 Responsible for more than 3 million deaths each year
 70 billion dollars for direct and indirect cost [2]
 The third leading cause of death worldwide
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Chronic Obstructive Pulmonary Disease (COPD)
COPD deaths worldwide in 2017 [2]
 Recent research: implications & economic burden of COPD can be reduced if we
protect patients against risk factors
4
COPD surveillance
Diabetes 2.6%
Chronic disease death rate (World Health Organization, 2018)
Sida 2.7%
Cancers 2.9%
COPD 5.8%
AVC 11.9%
Cardiac 13.2%
5.8%
COPD
Introduction
More than 1.7 million canadians reached COPD
Direct Cost / year: 1,4 billion dollars
coûts indirects/an: 1,8 milliard de dollars
In Canada
4th reason of deaths : 24 persons/day
1,5 million undiagnosed people
Why COPD
Map of Canada

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Motivation: Why IoT?
Pervasive computing through the IoT
technology creates a new opportunity
to redesign the traditional pattern of
medical system.
Time to design solutions
and devote technologies to
save our lives!!
5
Problems
Telemonitoring And COPD:
“telemedicine, both its application and results, is
still controversial in COPD and the monitoring of
physiological parameters does not solve the
problem of predicting exacerbations that could
lead to early therapy and prevention of hospital
admissions”
Dr. Jean Bourbeau, a senior scientist at
the research institute of McGill University, Canada
Health Center
6

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Medical visit:
 Diagnostic
 Prescription of drugs
The patient does not
respond well to treatment
After a
period of
time
Bad results:
change the drugs
A. How to make treatment plan more efficiency?
Traditional treatment method
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• Progress of the disease and response to treatment
New analyzes and medical
tests
General problem
How the treatment plan can avoid the worst patient’s condition, reduce transition
to severe phase, and decrease the morbidity and disability?
Environment
Activities
B. How to protect patients against risk factors?
Physical state
Psychological state
Nutrition
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Activité physique
• Amount of calcium
• Amount of magnesium
• Carbohydrate amount
• Quantity of fructose
• Sodium amount
• Amount of glucose
• Amount of Vitamin D
• Quantity of zinc
• Amount of vitamin A
• Amount of vitamin B
• Quantity of fiber
• Body temperature
• Blood pressure
• Heartrate
• Partial pressure oxygen (PaO2)
• Oxygen saturation (SpO2)
• Partial pressure carbon dioxide (PaO2)
• Oxygen consumption (VO2)
• Respiration rate
• PH
• HCO3
• FEV1
 Depression
 Anxiety
General problem
How to make sure that the treatment can reduce risk of those COPD factors (context) ?

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Problem : Complexity of medical contexte
Problem B:
How to protect patients against
risk factors?
Problem A:
How can the treatment be made
more effective in reducing the
transition to the severe phase
and reducing morbidity and
disability?
• Humidity
• Temperature
• Precipitation
• Atmospheric pressure
Indoor / outdoor weather
• Depression and Anxiety
Psychologic state
• Exercice
• Trip
Activity
• Infection
Physical state
• Smoke
• Dust
• Mites
• Pollen and fur
• Cleaners
Indoor / outdoor air
quality
Food
 drug response
• Diagnostic
• Prescription of
medication
After a
period of
time
Traditional processing mechanism
Bud results:
Change the
drug
Patient
COPD
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 Monitor the evolution of the COPD disease to avoid risks
 Monitor the patient's environment
 Evaluate the daily activities of a patient
 Avoid comorbidities
80
100
20
40
60
Stage I
Stage II
Stage III
Stage IV
Death
• A chronic cough
• increased production of sputum
• dyspnea on exertion
• Reduced tolerance to exercise
• Increased respiratory infections and exacerbations
• Respiratory failure
• Elevation of venous pressure
10 30
0 40 50 60 70 80 90 Age (Year)
Survival Normal Line
COPD Patient
• Decrease the intensity of symptoms
• Slow the progression of the disease
• Help the patient to live as normally as
possible
Goals
FEV1
Extend lifetime and improve quality life of patients as well as reduce economic burden

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Law of Weinberg
If builders made buildings
like programmers write
programs, the first
Woodpeckers to pass
would destroy civilization
Jerry Weinberg 1930
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Complexity of contemporary systems
Some complex systems
• Software systems become more complex:
• Desktop (static) -- > mobile computing
• Discrete -- > embedded & ubiquitous computing
• Autonomous -- > pools of nodes, Cloud computing
 To address this complexity, moderns systems can be designed using IoT
adaptive and intelligent architecture (Patterns, ADD, SPRING, OSGi,
MAPE, …) that can :
• add and remove features (Modularity, Dependency, Scalability, …)
• offer a variety of binding time
• respond quickly to changes (needs)
• facilitate reuse and maintenance
• use & predict relevant information (context)
• manage virtualization and interoperability, etc.
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Complexity and general solutions

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V. Jones, et al., “Biosignal and Context Monitoring: Distributed Multimedia Applications of Body Area Networks
in Healthcare,” 2008IEEE 10th Workshop on Multimedia Signal Processing, Cairns, 8-10 October 2008, pp. 820- 825.
13 Decembre 2016
Healthcare system architecture
IoT Pervasive computing
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 Context-aware systems (CAS) are a component of an IoT pervasive
computing environment

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IoT/M2M solution Architecture
Open Standards
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Atypical IoTsolution is characterized by many devices (i.e. things)
that may use some form of gateway to communicate through a
network to an enterprise back-end server that is running an IoT
platform that helps integrate the IoTinformation into the existing
enterprise. The roles of the devices, gateways, and cloud platform
are well defin
e
d, a
nd e
ach o
f th
em p
r ovi d
es s
peci fic
f
e
a
t ures an
d
functionality required by any robust IoTsolution.
5 The Three Software Stacks Required for IoTArchitectures
Importance of the
context
• Most actions would be judged based upon
contexts
• The context can change the entire
meaning of events
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Context
Two persons with guns in two different contexts

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In computer science
Computer with human senses
Advantages
• To be aware of surrounding world
• Making the right decision
• Providing adaptive services
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16th InternationalConference On Smart homes and health
Telematics
• Context aware system:
“is how to make the machine think like a human mind”, therefore the ability
to discover the real world and take advantage of contextual information,
through structured process.
Computers & devices can sense and react based on their environment
[Schilit,1994; Elmalaki et al., 2015]
• A system is context-aware if it uses context to provide relevant information
and/or services to the user, where relevancy depends on the user’s task.
• These systems have many issues such as: context definition, context
modeling, context processing (reasoning), interoperability, adaptation,
security, etc.
• We focus on some of these issues
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Context awareness

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In 2015 Li et al. defined the context :
is any piece of information that can represent changes of the circumstance
(either static or dynamic). Further, it could be useful for understanding the
current situation and predicting potential changes
Amyad, 2016, PhD thesis, ETS-Canada: combination of elements :
• Context (<element> <state> <value> <times> <location>)
• Context (<Adam> <presence> <active> <21:00> <room 1>)
Examples :
Temperature sensors to detect high Temperature
It will automatically call the fire brigade
Sensors to detect the level of energy consumption
Sensors to detect the blood pressure of patients
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Context definition and example
TELEMONITORING
✔What should be monitored?
✔What services should be provided?
✔How offer the effectiveness of telemonitoring?
20 BUT IS NOT ENOUGH
Existing Solutions for healthcare systems

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TELEMONITORING
× How to handle the multiscale nature of COPD?
× How to provide an accurate risk assessment and fast intervention?
× No comprehensive system has been provided.
× No fully automated application that handles the monitoring process and
takes precluding actions without involving human in interventions.
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Existing Solutions for COPD
The exacerbation or pulmonary crisis has a particular consequence in COPD, such as:
1. Death (Lareau et al., 2014).
2. Degradation of quality of life (Viegi et al., 2007).
3. The rapid deterioration of respiratory function in COPD (Burt and Corbridge,
2013).
In addition,
1. Exacerbation is the main factor in medical visits, represent at least 70% of all
COPD medical care costs (Trappenburg et al., 2008), (Quebec, 2016).
2. Only 50% of all exacerbations are reported to doctors (Seemungal et al., 1998).
3. Medication does not cure this disease but only dilates the bronchi to provide
more air to the alveoli (Canada, 2010).
Why the Informatics tools to detect exacerbations ?
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But, the rapid detection of an exacerbation helps to
reduce its effects and to facilitate the recovery of the
lungs (Wilkinson et al., 2004) (Trappenburg et al.,
2008).
Consequently,
Contexte aware application is a software solution that
can monitor daily and at home the patients, in order to
predict the risks of exacerbation.
Why the Informatics tools to detect exacerbation ?
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IoT Monitoring healthcare system
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IoT Monitoring healthcare architecture : layer style
Medical Context
Three dimensions approach to build our healthcare
system
Ontological Model Medical Rules
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Existing solutions of context
Authors Year Dimensions
Chen et al. 2004 Agents, time, space, events, user profiles, actions and policies.
Chaari et al. 2005 network profile, user description/preferences, terminal characteristics, location and
environment
Razzaque et al. 2005 user, physical, network, activity, material and service context
Miao et al. 2006 Sensed, profiled and derived
Chong et al. 2007 Computing, Physical, History, Identity and Time
Zimmerman 2007 Individuality, time, location, activity and relation
Miraoui et Tadj 2008 Trigger information, Quality changing information
Arianti Kurti 2009 User’s profile, activity, location/environment
Zhong 2009 User, System, Environment, Social, Time
Tamine et al. 2010 User, platform and environment.
Nageba E. 2011 Physical and abstract
Kim et al. 2012 5W1H (Who, When, Where, What, Why and How)
Bin Guo 2013 Individual, social, and urban context
Boughareb et al. 2014 Device, task, user, document, spatio-temporal, environmental, and event
Ameyed 2016 Time, space and purpose
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These classifications are complex and d not fit medical domain (no prediction)
To facilitate the use of the context, the researchers have classified
the contextual information into several categories and/or
parameters (dimensions) according to different perspectives:
terminologies
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Profile Time Localisation Environment Activity
Technology Event
Task Organization Disease
History
Politics
Real time data
4
3 5 6
7
8
9
10 11
12
13
14
Person
2
1
The medical context
 Facilitates the understanding of the context
 Allows more efficient representation for the context

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Ontology Model
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Localisation
Environment
Devices
Clinical state
Activity
Patient Service
Disease
At
a
Has
Engaged in
Suffer
from
uses
serv
e
Figure 5. Group of ontologies
Proposed ontology
 COPDOLOGY

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Parameters
Age
Gendre
BMI
Stage of disease
Comorbidity
Does age affect the parameter
Does gender affect the parameter
Does the BMI affect the parameter
Does the stage affect the parameter
Does comorbidityaffect the parameter
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Yes
Yes
Yes
Yes
Yes
Women Men
40-50 50-60 80-90
……
Stage 1 Stage 2 Stage 3 Stage 4
Asthma Diabetes Hypertension
…….…
<18 18-23 >25
Rule 1:
IF the patient between 40 and 50 years old, female, and her BMI less than 18, and her stage of COPD is 1, and she has
diabetes THEN Heart rate between 65 and 75 b / min
Rules extraction
13781 rules : vital signs
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Rules extraction
48 publications 105 publications 134 publications + analysis
Environmental Rules
Nutrition Rules
Activities Rules
3735 Rules
2844 Rules
968 Rules
Influencing factors:
• BMI
• Stage
• Comorbidity
Influencing factors:
• Age
• Gender
• BMI
• Stage
• Comorbidity
• Biomarkers
Influencing factors:
• Age
• Gender
• BMI
• Stage
• Comorbidity
• Smoker
• biomarkers
7547 rules : 968+2844+3735

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Profile
• Age: 68 year
• sex: Male
• stage: 2
• BMI: 22.8
• Comorbidity: diabetes
• dyspnea scale is 3
• Medication: Théophylline
+ insuline
COPD
Patient
Time
Activity
Location
100 mm Hg
80 mm Hg
40 mm Hg
Level of The partial
pressure of oxygen
Dangerous
Level
Normal
Dangerous
Level
No
condition
120 mm Hg
140 mm Hg
Define the normal thresholds
Personalization by profile and by constraints
Light
physical
efforts
Altitude =
0 meters
60 mm Hg
20 mm Hg
Intensive
physical
efforts
Altitude =
800 meters
Normal 20 minutes
Normal
Normal
What is
the level
PaO2?
Millimeter of
mercury
Testing with scenarios
Medical experts:
15 pulmonologists
5 general practitioners
Combining, processing and cleaning
data
Step 1: Data collection for the simulation model
Step 3: Test the data
Step 4
Assessment
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Results
Create an instance of ontology :
Injection of data into ontology and
application of SWRL rules (real-time)
Step 2: Synchronization and
Merging
Environmental
datasets
Electronic medical
records
Dataset of
simulated real life
activities
Experimentation

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Dr. Kevin Schwartzman
Respiratory Division
University of McGill
Dr. Harissios Vliagoftis
Professor of Medicine
University of Alberta
Dr. Michael Stickland
Division of Pulmonary
University of Alberta
Dr. Alim Hirji
Division of Pulmonary
University of Alberta
Dr. Christopher Carlsten
Division Head of Respiratory
medicine in UBC
Dr. Paul Hernandez
Division of Respirology
University of Dalhousie
Dr. Stephen Field
Clinical Professor
University of Calgary
Dr. Salman Mroueh
Professor of Medicine
AUBMC
Dr. M. Khanafer
General practitioner
Private clinic
Dr. Dean Befus
Division of Pulmonary
University of Alberta
 Evaluation – medical
Experimentation
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Intervention present Intervention absent Total
Index test positive True positive (TP)=512 False positive (FP)=88 TP+FP=600
Index test negative False negative (FN)=56 True negative (TN)=544 TN+FN=600
Total TP+FN=568 TN+FP=632
• Accuracy (AC)= (TP + TN) / (TN + FN + FP + TP) =88%
• Sensitivity = (TP) / (TP+FN) =91.14%
1200 records were sent to 20 doctors and pulmonologists to validate the
results of our system
Experimentation
TP: If both ontology and physicians suggested hospitalization
TN: If both ontology and physicians do not suggest hospitalization
FP: If ontology suggested hospitalization but in fact there was no need
FN: If ontology did not suggest hospitalization but the case were urgent

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 Designed a healthcare monitoring system to :
 Follow the evolution of the disease
 Monitor the daily activities and patient's environment
 Avoid comorbidities
 Designed rule-based system for COPD disease
 Validated this system using many data bases from different
sources : Environment, activity, and medical profile
 Main contribution : identify dynamic threshold of a patient
 Will apply our monitor system to more real-time cases and
using other diseases
Conclusion and Future Works
[1] Vogelmeier CF, Zriner GJ, Martinez FJ et al. Global strategy for the diagnosis, management,
and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Eur
Respir J. 2017; 49: 1750214
[2] Patel JG, Coutinho AD, Lunacsek OE, Dalal AA. COPD affects worker productivity
and health care costs. Int J Chron Obstruct Pulmon Dis. 2018;13:2301–2311. Published
2018 Jul 30. doi:10.2147/COPD.S163795
References