ICCES'2016 BIG DATA IN HEALTHCARE AND SOCIAL SCIENCES
1. Big Data in Healthcare and
Social Sciences
VICTORIA LÓPEZ
UNIVERSIDAD
COMPLUTENSE DE MADRID
Barcelona, Spain, 23-27 July- 2016 1
ICCES’2016
International Conference of Computing for Engineering and Sciences
2. Outline
Healthcare and Social Sciences
Information Privacy
Some projects (as examples)
◦ Bronchoopulmonary Dysplaxia
◦ Bipolar disorder
◦ Intelligent Integration of COhorts for a better life of DEMEntia patiens
◦ WAP Madrid
◦ Asperger disorder
The future is coming in 5 years…
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3. Healthcare and Social Sciences
Everybody, ….. Everybody … is sensible about healthcare.
No discusion about investing money/time/efforts
Clear goals for projects
Prevention vs. Curation (here the money is first)
Personal health: people are able to check their own rates
Healthcare becames a social issue
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Individual
knowledge
Social
contributions
Colective
knowledge
Social
Development
Less
effort
More
improvements
4. Healthcare and Social Sciences
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Individual
knowledge
Social
contributions
Colective
knowledge
Social
Development
Less
effort
More
improvements
Solutions come from predictive analytics
Predictive modeling
Machine learning
Data mining
GOAL:
PREVENTION
5. Heathcare and Big Data
Intelligent Data Analytics
Predictive analysis
Clustering
Pattern discovery
Visualization
FINE, www.finenet.nl
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Problem formulation – data exploring – hypothesis formulation – working with
data – experimentation – results – iteration
6. HealthCare and Big Data
Most of problems in healthcare are big data issues.
About volume
◦ Nowadays we can collect data all over the world
◦ We are being monitoring! … all the time!
◦ -Accelerometers
◦ -Smartphones
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7. Heathcare and Big Data
Most of problems in healthcare are big data issue
About velocity
◦ We need to know the crisis is coming in advance (asap)
◦ Real time must work as real time in real life
About variety
◦ Data is unstructured and diverse
◦ Data pre-processing
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8. Heathcare and Big Data
Most of problems in healthcare are big data issues.
About veracity
◦ Accuracity is a due!
◦ Not only money but also lifes
◦ Data cleaning and integration
◦ Noise and outlliers must be out of the sample
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10. Information privacy
The challenge of data privacy is tu utilize data while protecting
individual’s privacy preferences and their personally identifiable
information
- computer security
- data security
- information security
Privacy becomes a big deal when health personal data is involved.
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11. Information privacy
Take into account:
Where storing data (cloud, inHouse, hybrid)
Who is allowed to see the data
How the data can be accessed
Is our project under the law?
New techniques:
◦ Data Anonymization
◦ Cryptography
◦ Disjoin private – public data
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12. Information privacy
Data Anonymization
- Encrypting all or part of the data
- Removing sensible data
Personally identifiable information from data sets
Technology that converts clear text data into a nonhuman readable and
irreversible form (no de-anonymization)
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13. Some projects
◦ Bronchoopulmonary Dysplaxia
◦ Bipolar disorder
◦ Intelligent Integration of COhorts for a better life of
DEMEntia patiens
◦ Wap Madrid
◦ Asperger disorder
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14. BRONCHOPULMONARY DYSPLASIA
PULMESCELL & PULMESGEN: GENETIC AND MOLECULAR BIOMARKERS PREDINCTING
BRONCHOPULMONARY DYSPLASIA AND THE INDIVIDUAL RESPONSE TO MESENCHYMAL STEM
CELL THERAPY
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15. BRONCHOPULMONARY DYSPLASIA
PULMESCELL & PULMESGEN: GENETIC AND MOLECULAR BIOMARKERS PREDINCTING
BRONCHOPULMONARY DYSPLASIA AND THE INDIVIDUAL RESPONSE TO MESENCHYMAL STEM
CELL THERAPY
Barcelona, Spain, 23-27 July- 2016 15
16. BRONCHOPULMONARY DYSPLASIA
PULMESCELL & PULMESGEN: GENETIC AND MOLECULAR BIOMARKERS PREDINCTING
BRONCHOPULMONARY DYSPLASIA AND THE INDIVIDUAL RESPONSE TO MESENCHYMAL STEM
CELL THERAPY
Barcelona, Spain, 23-27 July- 2016 16
17. BRONCHOPULMONARY DYSPLASIA
INGEMM
Pablo Lapunzina
INSTITUTE GENETICS HURYC
Javier Moreno
STEM CELL PRODUCTION
Manuel Ramirez Orellana
Modelos Animales
Javier Garcia Castro
La Fe Hospital
IP: Max Vento
La Paz Hospital
IP: Paloma López
D Elorza, C labrandero y Marta C.
Clinico Hospital
IP: Luis arruza
Santiago rueda y Cristina Glez Menchen
Vall d Hebron Hospital
IP: Felix castillo
Antonio Moreno Galdo (neumo)
y Anna sabaté (cardio)
H.U. RAMON Y CAJAL IP: M. Jesús del Cerro
Maria Álvarez, Cristina Sanchez, E. Garrido
BIG DATA. Grupo GTeC - GRASIA
Informática Complutense
IP: Victoria López
y Guadalupe Miñana
Biomarkers
IP: Laura Moreno
Jane Mitchell,
Francisco Vizcaino
COST BENEFIT ANALYSIS
CSIAT IP: FJ Diaz UNED
CHUAC Hospital (A Coruña)
IP: Alejandro avila
C Blanco
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18. BIPOLAR DISORDER
CAD prediction system for bipolar disorder, a complex mental disorder in which the
core feature is disturbance in mood, ranging from extreme elation (mania) to severe
depression, accompanied by disturbances in thinking and behaviour.
2% of the world’s population. The disease has substantial consequences both for the
individual and for health care spending.
The World Health Organization has identified BD as the sixth leading cause of
disability-adjusted life years in the world. In 2009, the direct and indirect costs of BD
were estimated to be US$151 billion. In the UK, the estimated national cost of bipolar
disorder was £4.59 billion per year. Hospitalisation during acute episodes costs £69
million per year.
Biomedical informatics methods are needed to process data and form
recommendations and/or predictions to assist decision makers in the form of
computer-aided decision (CAD) support.
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23. H2020: I2-CODEME (Intelligent Integration of
COhorts for a better life of DEMEntia patiens)
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I2-CODEME consortium will provide a set of
cohorts related with dementia and other
neurodegenerative diseases that in particular will
improve quality of life of patients by establishing
home treatment protocols and it is expected that
will decrease by around 20% the costs associated
to treatments from the patient point of view.
I2-CODEME Ecosystem
Health
& Social
Care
Policy
Makers
Pa ents
&
Families
Figure 1. I2-CODEME Ecosystem Actors
24. H2020: I2-CODEME (Intelligent Integration of
COhorts for a better life of DEMEntia patiens)
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As a result of the methodology
applied, I2-CODEME will establish a
set of KPIs (key performance
indicators) from different
perspectives (medical, healthcare,
social care, policies).
25. H2020: I2-CODEME (Intelligent Integration of
COhorts for a better life of DEMEntia patiens)
Barcelona, Spain, 23-27 July- 2016 25
Cost associated to early detection of dementia patients
26. H2020: I2-CODEME (Intelligent Integration of
COhorts for a better life of DEMEntia patiens)
Barcelona, Spain, 23-27 July- 2016 26
Relationship between different machine learning frameworks
27. H2020: I2-CODEME (Intelligent Integration of
COhorts for a better life of DEMEntia patiens)
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A platform on Open Data. Getting value from healthcare data.
31. Asperger syndrome
Asperger syndrome (AS) is a developmental
disorder characterized by significant difficulties
in social interaction and nonverbal communication,
along with restricted and repetitive patterns of
behavior and interests. It is an autism spectrum
disorder (ASD) and differs from other disorders by
relatively normal language and intelligence.
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