CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities

CARRE project
CARRE projectCARRE project
HealthInf 2016: 9th International Conference on Health Informatics
BIOSTEC, Rome, Italy, 21-23 February, 2016
CARRE
Personalized patient empowerment and shared decision
support for cardiorenal disease and comorbidities
Eleni Kaldoudi
CARRE Coordinator
Associate Professor
School of Medicine, Democritus University of Thrace, Greece
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
motivation
 significant increase in the prevalence and incidence
of chronic disease
 ½ of all chronic patients
present comorbidities
 the chronic patient is mostly
an outpatient
 needs to care for herself at home
 mainly away from
continuous professional care
 while trying to lead a normal life
prevent
detect
manage
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
medical domain
chronic cardiorenal disease and comorbidities
 simultaneous (causal) dysfunction of kidney and heart
 diabetes and/or hypertension common underlying causes
 a number of other serious comorbidities often present
nephrogenic anemia, renal osteodystrophy, malnutrition,
blindness, neuropathy, severe atherosclerosis,
cardiovascular episodes, and eventually
end-stage renal disease and/or heart failure,
and death
 deterioration to end stage renal/heart disease is
life threatening, irreversible and expensive to manage
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
cardiorenal disease & comorbidities
some numbers…
 hypertension  1/3 of adults (US 2008)
 diabetes  8% of overall population
 chronic kidney disease  9-16% of overall population
 44% of chronic kidney disease is due to diabetes
 86% of chronic kidney disease has at least 1 comorbidity
 most patients with chronic kidney disease develop
cardiovascular disease
 chronic heart failure  1-2% of total healthcare costs
 end-stage renal disease (dialysis)  >2% of total healthcare costs
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
CARRE
Cardiorenal
comorbidity management
via empowerment and
shared informed decision
FP7-ICT-2013-611140
consortium: 6 partners from 4 EU countries
coordinator: Eleni Kaldoudi (DUTH)
duration: Nov 2013 – Oct 2016
budget: 3,210,470€
http://carre-project.eu/
Democritus Univ. of Thrace
DUTH, GR
The Open
University, UK
Univ. of Bedfordshire, UK Vilnius Univ. Hospital, LT
Kaunas Univ., LT
Industrial Research Institute
for Automation & Measurements, PL
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
CARRE
approach
foster understanding of comorbid condition
calculate informed comorbidity progression
compile personalized empowerment services
support shared informed decision and
integrated management
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
medical evidence
aggregation
evidence based
medical literature
Educational
resources
…
social media
personal health
information
quantified
self
weight
physical activity
blood pressure
glucose
CARRE approach
private
public
data harvesting
& interlinking
LOD
comorbidity model visualization
(generic and personalized)
patient empowerment &
decision support services
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
risk factor as a central concept
disorder 1
(as a risk factor)
disorder 2
(as a probable
consequence)
leads to
under certain
conditions
with a
probability x
E. Kaldoudi, et al. CARRE D.2.1, 2014, www.carre-project.eu
risk factors are reported in medical literature
(top level evidence: systematic reviews with meta-analysis)
characterizes
is a value of
type of risk
element
biomedical
ratio value confidence intervalratio type adjustment for
risk factor as a central concept
E. Kaldoudi, et al. CARRE D.2.2, 2014
risk
element
observable
observable
condition
satisfies
observable
condition
1…N
determines
risk evidence
1…N
has
risk
ratio
risk ratio evidence source
has
evidence
source
source
risk
element
target
risk
element
causes, is an
issue in, …
risk evidence
demographic
genetic
behavioural
intervention
environmental
is a value of
1…N
measures the
state of risk
element
condition disorder
A. Third, E. Kaldoudi, G. Gotsis, S. Roumeliotis, K. Pafili, J. Domingue,
Capturing Scientific Knowledge on Medical Risk Factors, K-CAP2015, ACM, NY, USA, Oct. 7-10, 2015
CARRE risk factor ontology
conceptual model
G. Gkotis, A. Third, CARRE D.2.4, 2014, www.carre-project.eu
CARRE risk factor ontology
CARRE ontology published in NCBO BioPortal
http://bioportal.bioontology.org/ontologies/CARRE
G. Gkotis, A. Third, CARRE D.2.4, 2014, www.carre-project.eu
risk factor identification methodology
search ground knowledge
to identify major risk factors
(guidelines and their literature: KDIGO,
KDOQI, ACC/AHA, NICE, ESC, EASD, ADA)
if result found
include all risk evidences
from the most recent
yes
search PubMed:
condition A AND condition B
(limited to systematic reviews
with metaanalyses
identify major
risk factors
(keywords)
search PubMed:
condition A AND
condition B
noif result found
include relevant risk evidence
from latest and highest level
yes
search again for next
update (1 year)
no
E. Kaldoudi, et al. CARRE D.2.2, 2014
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
some of the major related conditions
1. Acute kidney injury
2. Acute myocardial infarction
3. Age
4. Albuminuria
5. Anaemia
6. Angina pectoris
7. Asthma
8. Atrial fibrillation
9. Chronic kidney disease
10. Chronic obstructive
pulmonary disease
11. Cholelithiasis
12. Colorectal Cancer
13. Coronary and carotid
revascularisation
14. Death
15. Depression
16. Diabetes
17. Diabetic nephropathy
18. Drugs
19. Dyslipidemia
20. Family history
21. Heart Failure
22. Hyperkalemia
23. Hypertension
24. Hyperuricemia
25. Hypoglycaemia
26. Ischemic heart disease
27. Ischemic stroke
28. Left ventricular hypertrophy
29. Obesity
30. Obstructive Sleep Apnoea
31. Myocardial infarction
32. Osteoarthritis
33. Pancreatic Cancer
34. Peripheral Arterial Disease
35. Physical activity
36. Smoking
37. …
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
medical evidence aggregator
https://www.carre-project.eu/innovation/medical-evidence-aggregator/
E. Liu, et al. CARRE D.3.4, 2015
sentence splitter
tokenizer
(GATE &Jape)
part-of-speech
tagging
dependency parsing
semantic role
labelling
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
medical evidence aggregator
https://www.carre-project.eu/innovation/medical-evidence-aggregator/
E. Liu, et al. CARRE D.3.4, 2015
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
obesity diabetescauses
when 23BMI34
risk ratio = 1.61
obesity hypertensioncauses
when 99.4  Waist Circumference  106.2 AND sex=male
risk ratio = 2.50
obesity heart failurecauses
when 25  BMI  30 AND sex=female
risk ratio = 2.50
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
hypertension
chronic renal
diseasecauses
when systolic BB  140mmHg AND/OR diastolic BB  90 mmHg
risk ratio = 2.00
smoking
chronic renal
disease
is an issue in
when smoking status = current AND sex=male
risk ratio = 2.40
so far… 253 major risk associations (or evidences) identified in medical literature
(which involve 53 health conditions and 82 related observables)
as included in the CARRE risk factor database and predictive model
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
http://entry.carre-project.eu/
CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities
CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities
http://entry.carre-project.eu/
CARRE D.5.3, 2016 (in progress)
http://entry.carre-project.eu/
CARRE D.5.3, 2016 (in progress)
CARRE D.5.3, 2016 (in progress)
CARRE D.5.3, 2016 (in progress)
CARRE D.5.3, 2016 (in progress)
CARRE D.5.3, 2016 (in progress)
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
medical evidence
aggregation
evidence based
medical literature
Educational
resources
…
social media
personal health
information
quantified
self
weight
physical activity
blood pressure
glucose
CARRE approach
private
public
data harvesting
& interlinking
LOD
comorbidity model visualization
(generic and personalized)
patient empowerment &
decision support services
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
personal data aggregators (WP3)
 sensor aggregators
 medical data aggregators from personal health record
 manual entry system for personal medical data
 intention extraction form web searches
CARRE D.3..2 & D.3.3, 2015
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
project site  innovation  breakthroughs
https://www.carre-project.eu/innovation/breakthroughs/
CARRE D.3..2 & D.3.3, 2015
aggregator integration
https://carre.kmi.open.ac.uk/devices/
CARRE D.3..2 & D.3.3, 2015
CARRE sensor innovation
 multiparametric CARRE Weight Scale - body fluid
balance monitoring, other parameters
 ECG aggregator – atrial fibrillation detection
 CARRE Wristwatch – heart arrhytmia detection, …
Paliakaite B., Daukantas S., Sakalauskas A., Marozas V., "Estimation of pulse arrival time using impedance plethysmogram from body composition scales," in Sensors
Applications Symposium (SAS), 2015 IEEE, pp.1-4, 13-15 April 2015
Paliakaitė B., Daukantas S., Marozas V., Assessment of Pulse Arrival Time for Arterial Stiffness Monitoring on Body Composition Scales, Computers in Biology and Medicine,
submitted to special issue: Self-monitoring systems for personalized health-care and lifestyle surveillance, 05/10/2015.
Petrenas, V. Marozas, L. Sörnmo, Low-complexity detection of atrial fibrillation in continuous long-term monitoring, Computers in Biology and Medicine, vol. 35, iss. 47, pp.
3365-3376, 2015.
Stankevicius et al. „Photoplethysmography based system for atrial fibrillation detection during hemodialysis“, submited to Medicon 2016
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
medical evidence
aggregation
evidence based
medical literature
Educational
resources
…
social media
personal health
information
quantified
self
weight
physical activity
blood pressure
glucose
CARRE approach
private
public
data harvesting
& interlinking
LOD
comorbidity model visualization
(generic and personalized)
patient empowerment &
decision support services
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
decision support services
interlace personal data and
medical evidence for personalized
services to
 plan
 monitor
 alert
 educate
CARRE D.6.2 & D.6.3, 2016 (in progress)
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
educational aggregator
https://edu.carre-project.eu/
Resource Retriever
Query Generator
Educational Object Harvester
Medical knowledge data aggregator Backend
Query Terms Extractor
Educational Object
Metadata Extractor
Metadata Enrichment and
Mapping to CARRE schema
Expert
Application
Frontend – user interface
Educational Object
Rating Module
Educational Metadata Sender
CARRE Server
public RDF
Resource Rating
Module
educationalrepositories
Local Storage
of metadataResource Metadata Processing
semanticweb
CARRE D.3.4, 2015
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
medical evidence
aggregation
evidence based
medical literature
Educational
resources
…
social media
personal health
information
quantified
self
weight
physical activity
blood pressure
glucose
CARRE approach
private
public
data harvesting
& interlinking
LOD
comorbidity model visualization
(generic and personalized)
patient empowerment &
decision support services
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
project site  innovation  breakthroughs
https://www.carre-project.eu/innovation/breakthroughs/
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
public RDF SPARQL endpoint
a SPARQL query to retrieve RDF
triples about educational objects
A. Third et al, CARRE D.4.1 & D.4.2, 2015
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
triples about educational objects
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
restful API
A. Third et al, CARRE D.4.1 & D.4.2, 2015
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
evaluation framework & plan*
CARRE system
functions
Human perspectives Context and
EnvironmentExperts Patients Admins
Structure
aggregators and
interfaces functioning
changes to
working conditions
and practices; new
skills, & abilities
new skills, and
abilities
Process
service operation
correct & valid
induced changes
in function and
satisfaction
Induced changes in
self-management &
satisfaction
Outcome
service usable and
reliable
effectiveness
perceived quality of
care and life
improving
specific
clinical
parameters
potential to
improve the health
status and quality
of life
1: component testing 2: service testing & understanding 3: service evaluation
* based on the model proposed by Cornford, T., Doukidis, G.I., and Forster, D., Int. J. Manag. Science, 22(5), 491-504, 1994
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
evaluation
 2 center
randomized control trial
 primary objectives
 increase health literacy
 increase level of patient empowerment (SUSTAINS instrument)
 improve quality of life (SF-36 instrument)
 reduce personal risk for cardiorenal disease and comorbidities
 improve or prevent disease progression
 improve lifestyle habits
 limit no. or dose of necessary drugs
 assess intervention acceptability
study population
for each pilot site
(total = 160 patients)
group 1
patients at risk
of heart or renal disease
(80 patients)
CARRE
intervention
(40 patients)
control
group
(40 patients)
group 2
patients with
heart or renal disease
(80 patients)
CARRE
intervention
(40 patients)
control
group
(40 patients)
primarysecondary
(biomarkers &
disease prevalence)
(sensor readings)
(dose of essential drugs)
(clinical & laboratory parameters)
CARRE D.7.4, 2016 (in progress)
what? CARRE
EU FP7-ICT-2013-611140
3.2M, 2013-2016
DUTH, OU, BED, VULSK, KTU, PIAP
why? cardiorenal disease
chronic, common, dangerous,
expensive, with many causing
factors and complex progression
how?
quantified self
medical evidence
personal
risk
prediction
personal
decision
support
for the patient for the medical expert
Processor
Real time clock &
Calendar
Micro SD
Card
4GB
Electrocardiogram
I, II, III leads
TI front-end
ADS1294DRL
RA
LL
LA
BlueTooth
Power
LA
RA
LL
Body Composition /
Weight Scale
TI front-end
AFE4300
LCD
WiFi
CARRE
server
sensor developments
for the ICT expert
Personalized patient empowerment and shared decision
support for cardiorenal disease and comorbidities
thank you !!!
acknowledgment
work funded under project CARRE
co-funded by the
European Commission under the
Information and Communication Technologies (ICT)
7th Framework Programme
Contract No. FP7-ICT-2013-611140
CARRE: Personalized patient empowerment
and shared decision support
for cardiorenal disease and comorbidities
http://www.carre-project.eu/
HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi
Contact
Eleni Kaldoudi
Associate Professor
School of Medicine
Democritus University of Thrace
Dragana, Alexandroupoli
68100 Greece
Tel: +302551030329
Tel: +30 6937124358
Email: kaldoudi@med.duth.gr
Email: carre@med.duth.gr
Cite as
Eleni Kaldoudi
CARRE: Personalized patient empowerment
and shared decision support
for cardiorenal disease and comorbidities
Presentation & Demo
HealthInf 2016: 9th International Conference
on Health Informatics, part of BIOSTEC,
Rome, Italy, 21-23 February, 2016
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CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities

  • 1. HealthInf 2016: 9th International Conference on Health Informatics BIOSTEC, Rome, Italy, 21-23 February, 2016 CARRE Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities Eleni Kaldoudi CARRE Coordinator Associate Professor School of Medicine, Democritus University of Thrace, Greece
  • 2. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi motivation  significant increase in the prevalence and incidence of chronic disease  ½ of all chronic patients present comorbidities  the chronic patient is mostly an outpatient  needs to care for herself at home  mainly away from continuous professional care  while trying to lead a normal life prevent detect manage
  • 3. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical domain chronic cardiorenal disease and comorbidities  simultaneous (causal) dysfunction of kidney and heart  diabetes and/or hypertension common underlying causes  a number of other serious comorbidities often present nephrogenic anemia, renal osteodystrophy, malnutrition, blindness, neuropathy, severe atherosclerosis, cardiovascular episodes, and eventually end-stage renal disease and/or heart failure, and death  deterioration to end stage renal/heart disease is life threatening, irreversible and expensive to manage
  • 4. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi cardiorenal disease & comorbidities some numbers…  hypertension  1/3 of adults (US 2008)  diabetes  8% of overall population  chronic kidney disease  9-16% of overall population  44% of chronic kidney disease is due to diabetes  86% of chronic kidney disease has at least 1 comorbidity  most patients with chronic kidney disease develop cardiovascular disease  chronic heart failure  1-2% of total healthcare costs  end-stage renal disease (dialysis)  >2% of total healthcare costs
  • 5. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi CARRE Cardiorenal comorbidity management via empowerment and shared informed decision FP7-ICT-2013-611140 consortium: 6 partners from 4 EU countries coordinator: Eleni Kaldoudi (DUTH) duration: Nov 2013 – Oct 2016 budget: 3,210,470€ http://carre-project.eu/ Democritus Univ. of Thrace DUTH, GR The Open University, UK Univ. of Bedfordshire, UK Vilnius Univ. Hospital, LT Kaunas Univ., LT Industrial Research Institute for Automation & Measurements, PL
  • 6. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi CARRE approach foster understanding of comorbid condition calculate informed comorbidity progression compile personalized empowerment services support shared informed decision and integrated management
  • 7. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  • 8. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi risk factor as a central concept disorder 1 (as a risk factor) disorder 2 (as a probable consequence) leads to under certain conditions with a probability x E. Kaldoudi, et al. CARRE D.2.1, 2014, www.carre-project.eu risk factors are reported in medical literature (top level evidence: systematic reviews with meta-analysis)
  • 9. characterizes is a value of type of risk element biomedical ratio value confidence intervalratio type adjustment for risk factor as a central concept E. Kaldoudi, et al. CARRE D.2.2, 2014 risk element observable observable condition satisfies observable condition 1…N determines risk evidence 1…N has risk ratio risk ratio evidence source has evidence source source risk element target risk element causes, is an issue in, … risk evidence demographic genetic behavioural intervention environmental is a value of 1…N measures the state of risk element condition disorder A. Third, E. Kaldoudi, G. Gotsis, S. Roumeliotis, K. Pafili, J. Domingue, Capturing Scientific Knowledge on Medical Risk Factors, K-CAP2015, ACM, NY, USA, Oct. 7-10, 2015
  • 10. CARRE risk factor ontology conceptual model G. Gkotis, A. Third, CARRE D.2.4, 2014, www.carre-project.eu
  • 11. CARRE risk factor ontology CARRE ontology published in NCBO BioPortal http://bioportal.bioontology.org/ontologies/CARRE G. Gkotis, A. Third, CARRE D.2.4, 2014, www.carre-project.eu
  • 12. risk factor identification methodology search ground knowledge to identify major risk factors (guidelines and their literature: KDIGO, KDOQI, ACC/AHA, NICE, ESC, EASD, ADA) if result found include all risk evidences from the most recent yes search PubMed: condition A AND condition B (limited to systematic reviews with metaanalyses identify major risk factors (keywords) search PubMed: condition A AND condition B noif result found include relevant risk evidence from latest and highest level yes search again for next update (1 year) no E. Kaldoudi, et al. CARRE D.2.2, 2014
  • 13. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi some of the major related conditions 1. Acute kidney injury 2. Acute myocardial infarction 3. Age 4. Albuminuria 5. Anaemia 6. Angina pectoris 7. Asthma 8. Atrial fibrillation 9. Chronic kidney disease 10. Chronic obstructive pulmonary disease 11. Cholelithiasis 12. Colorectal Cancer 13. Coronary and carotid revascularisation 14. Death 15. Depression 16. Diabetes 17. Diabetic nephropathy 18. Drugs 19. Dyslipidemia 20. Family history 21. Heart Failure 22. Hyperkalemia 23. Hypertension 24. Hyperuricemia 25. Hypoglycaemia 26. Ischemic heart disease 27. Ischemic stroke 28. Left ventricular hypertrophy 29. Obesity 30. Obstructive Sleep Apnoea 31. Myocardial infarction 32. Osteoarthritis 33. Pancreatic Cancer 34. Peripheral Arterial Disease 35. Physical activity 36. Smoking 37. …
  • 14. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregator https://www.carre-project.eu/innovation/medical-evidence-aggregator/ E. Liu, et al. CARRE D.3.4, 2015 sentence splitter tokenizer (GATE &Jape) part-of-speech tagging dependency parsing semantic role labelling
  • 15. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregator https://www.carre-project.eu/innovation/medical-evidence-aggregator/ E. Liu, et al. CARRE D.3.4, 2015
  • 16. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi obesity diabetescauses when 23BMI34 risk ratio = 1.61 obesity hypertensioncauses when 99.4  Waist Circumference  106.2 AND sex=male risk ratio = 2.50 obesity heart failurecauses when 25  BMI  30 AND sex=female risk ratio = 2.50
  • 17. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi hypertension chronic renal diseasecauses when systolic BB  140mmHg AND/OR diastolic BB  90 mmHg risk ratio = 2.00 smoking chronic renal disease is an issue in when smoking status = current AND sex=male risk ratio = 2.40 so far… 253 major risk associations (or evidences) identified in medical literature (which involve 53 health conditions and 82 related observables) as included in the CARRE risk factor database and predictive model
  • 18. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi http://entry.carre-project.eu/
  • 31. CARRE D.5.3, 2016 (in progress)
  • 33. CARRE D.5.3, 2016 (in progress)
  • 34. CARRE D.5.3, 2016 (in progress)
  • 35. CARRE D.5.3, 2016 (in progress)
  • 36. CARRE D.5.3, 2016 (in progress)
  • 37. CARRE D.5.3, 2016 (in progress)
  • 38. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  • 39. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi personal data aggregators (WP3)  sensor aggregators  medical data aggregators from personal health record  manual entry system for personal medical data  intention extraction form web searches CARRE D.3..2 & D.3.3, 2015
  • 40. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi project site  innovation  breakthroughs https://www.carre-project.eu/innovation/breakthroughs/ CARRE D.3..2 & D.3.3, 2015
  • 42. CARRE sensor innovation  multiparametric CARRE Weight Scale - body fluid balance monitoring, other parameters  ECG aggregator – atrial fibrillation detection  CARRE Wristwatch – heart arrhytmia detection, … Paliakaite B., Daukantas S., Sakalauskas A., Marozas V., "Estimation of pulse arrival time using impedance plethysmogram from body composition scales," in Sensors Applications Symposium (SAS), 2015 IEEE, pp.1-4, 13-15 April 2015 Paliakaitė B., Daukantas S., Marozas V., Assessment of Pulse Arrival Time for Arterial Stiffness Monitoring on Body Composition Scales, Computers in Biology and Medicine, submitted to special issue: Self-monitoring systems for personalized health-care and lifestyle surveillance, 05/10/2015. Petrenas, V. Marozas, L. Sörnmo, Low-complexity detection of atrial fibrillation in continuous long-term monitoring, Computers in Biology and Medicine, vol. 35, iss. 47, pp. 3365-3376, 2015. Stankevicius et al. „Photoplethysmography based system for atrial fibrillation detection during hemodialysis“, submited to Medicon 2016
  • 43. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  • 44. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi decision support services interlace personal data and medical evidence for personalized services to  plan  monitor  alert  educate CARRE D.6.2 & D.6.3, 2016 (in progress)
  • 45. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi educational aggregator https://edu.carre-project.eu/ Resource Retriever Query Generator Educational Object Harvester Medical knowledge data aggregator Backend Query Terms Extractor Educational Object Metadata Extractor Metadata Enrichment and Mapping to CARRE schema Expert Application Frontend – user interface Educational Object Rating Module Educational Metadata Sender CARRE Server public RDF Resource Rating Module educationalrepositories Local Storage of metadataResource Metadata Processing semanticweb CARRE D.3.4, 2015
  • 46. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi medical evidence aggregation evidence based medical literature Educational resources … social media personal health information quantified self weight physical activity blood pressure glucose CARRE approach private public data harvesting & interlinking LOD comorbidity model visualization (generic and personalized) patient empowerment & decision support services
  • 47. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi project site  innovation  breakthroughs https://www.carre-project.eu/innovation/breakthroughs/
  • 48. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi public RDF SPARQL endpoint a SPARQL query to retrieve RDF triples about educational objects A. Third et al, CARRE D.4.1 & D.4.2, 2015
  • 49. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi triples about educational objects
  • 50. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi restful API A. Third et al, CARRE D.4.1 & D.4.2, 2015
  • 51. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi evaluation framework & plan* CARRE system functions Human perspectives Context and EnvironmentExperts Patients Admins Structure aggregators and interfaces functioning changes to working conditions and practices; new skills, & abilities new skills, and abilities Process service operation correct & valid induced changes in function and satisfaction Induced changes in self-management & satisfaction Outcome service usable and reliable effectiveness perceived quality of care and life improving specific clinical parameters potential to improve the health status and quality of life 1: component testing 2: service testing & understanding 3: service evaluation * based on the model proposed by Cornford, T., Doukidis, G.I., and Forster, D., Int. J. Manag. Science, 22(5), 491-504, 1994
  • 52. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi evaluation  2 center randomized control trial  primary objectives  increase health literacy  increase level of patient empowerment (SUSTAINS instrument)  improve quality of life (SF-36 instrument)  reduce personal risk for cardiorenal disease and comorbidities  improve or prevent disease progression  improve lifestyle habits  limit no. or dose of necessary drugs  assess intervention acceptability study population for each pilot site (total = 160 patients) group 1 patients at risk of heart or renal disease (80 patients) CARRE intervention (40 patients) control group (40 patients) group 2 patients with heart or renal disease (80 patients) CARRE intervention (40 patients) control group (40 patients) primarysecondary (biomarkers & disease prevalence) (sensor readings) (dose of essential drugs) (clinical & laboratory parameters) CARRE D.7.4, 2016 (in progress)
  • 53. what? CARRE EU FP7-ICT-2013-611140 3.2M, 2013-2016 DUTH, OU, BED, VULSK, KTU, PIAP why? cardiorenal disease chronic, common, dangerous, expensive, with many causing factors and complex progression how? quantified self medical evidence personal risk prediction personal decision support for the patient for the medical expert Processor Real time clock & Calendar Micro SD Card 4GB Electrocardiogram I, II, III leads TI front-end ADS1294DRL RA LL LA BlueTooth Power LA RA LL Body Composition / Weight Scale TI front-end AFE4300 LCD WiFi CARRE server sensor developments for the ICT expert Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities
  • 55. acknowledgment work funded under project CARRE co-funded by the European Commission under the Information and Communication Technologies (ICT) 7th Framework Programme Contract No. FP7-ICT-2013-611140 CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities http://www.carre-project.eu/
  • 56. HealthInf 2016, Rome, 23 Feb 2016 E. Kaldoudi Contact Eleni Kaldoudi Associate Professor School of Medicine Democritus University of Thrace Dragana, Alexandroupoli 68100 Greece Tel: +302551030329 Tel: +30 6937124358 Email: kaldoudi@med.duth.gr Email: carre@med.duth.gr Cite as Eleni Kaldoudi CARRE: Personalized patient empowerment and shared decision support for cardiorenal disease and comorbidities Presentation & Demo HealthInf 2016: 9th International Conference on Health Informatics, part of BIOSTEC, Rome, Italy, 21-23 February, 2016