towards acost-effective homecare for acaregiver assistance ... · towards acost-effective homecare...
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Slide 1Slide 1
Towards a Cost-Effective Homecare for
a Caregiver Assistance System in Brazil
MAURO OLIVEIRA
LAR-AComputer Network Laboratory of Aracati
Slide 216th Healthcom October 16th, 2014 Natal - RN Slide 2
Mauro OliveiraFederal Institute of Ceará (IFCE)
Aracati, [email protected]
Odorico AndradeFederal University of Ceara (UFC)
Fortaleza, [email protected]
Marcos SantosState University of Ceara (UECE)
Fortaleza, [email protected]
Roberto AlcântaraFederal Institute of Ceará (IFCE)
Aracati, [email protected]
Germanno TelesNortheast Bank of Brazil(BNB)
Fortaleza, [email protected]
Nazim AgoulmineJoseph Fourier University (UJF) Université d´Evry Val dÉssone [email protected]
evry.fr
Towards a Cost-Effective Homecare for
a Caregiver Assistance System In Brazil
Team working on this project
Slide 3
1. Contextualization
2. Application Scenario
3. Brazilian Digital TV
4. LARIISA Project
5. Prototype
Conclusion
Summary
Slide 4
1. Contextualization
Slide 5
Health System - Information EraBased on Disease PREVENTIONC
ost
s
¢
Encouraged
Discouraged Fonte: K. Jennings, K. Miller, S. Materna (1997)
Hospital (specialits)
Health Agent
Primary
Health Care
$
CONTEXTUALIZATION
Descentralization of PublicHealth System
Slide 6
PROBLEM
ManagementInformation
Message
Data
Acquisition
CONTEXTUALIZATION �
Increasing Complexity ofHealth Management for
Decision-making
Slide 7
CONTEXT-AWARE FRAMEWORK
SOLUTION
ONTOLOGY
BAYESIAN
NETWORK
MetadataGeolocation
Decision-Making
Information
Health Knowledge
InferenceMechanism
PROBLEM �CONTEXTUALIZATION �
LARIISA: an Intelligent System tosupport decision-making process
DATA MINING
Slide 8
Data Acquisition(Patient CONTEXT)
DECISION-MAKING
LARIISA’s Scenario: a context-aware framework
METADATA
Option 1
Option 3
Option 2
LARIISA: A Context-Aware Framework
ONTOLOGY BAYESIAN NETWORKS
DATA MINING
Slide 9
2. Application Scenario
Slide 10
Scenario for LARIISA ApplicationCAREGIVER - an unpaid or paid person who helps another individual
with an impairment with his or her activities of daily living.
2. Motivation :The Brazilian Digital TV
LARIISA can helpSpecialized person
Or NON specialized person
Slide 11Prague, Czech Republic July/2013
Data acquisition - CONTEXT
Slide 12
LARIISA Data Acquisition
Slide 13
ONTOLOGY BAYESIAN NETWORKS
DARA MININGData Acquisition
(CONTEXT)METADATA
DECISION-MAKING
LARIISA: Next Generation
Slide 14
3. The Brazilian Digital TV
Slide 15
AnalógicoAnalógico
Today Interactive Digital TV
The Brazilian Digital TV
Slide 16
Network
Audio
Video
Data
Data Carrossel
1. MOTIVATION
Interactive Digital TV
Slide 17
Interactive Digital TV
The Brazilian Digital TV
Slide 18Prague, Czech Republic July/2013
Architecture of the Brazilian Digital TV
GINGA, a recommendation H.761 of the International Telecommunications Union (ITU-T).
Slide 19
4. LARIISA Project
Slide 20
Medical Sensors
Humidity SensorAtmosphericPressure Sensor
Temperature SensorDigital CameraGlobal Positioning
System (GPS)AccelerometerInternet ConnectionLight SensorProximity SensorCompassGyroscope
GeographicalInformation System (GIS)
LARIISA: a Context-Aware Framework
Slide 2115th Healthcom October 10th, 2013 Lisbon, Portugal Slide 21
LARIISA: a Context-Aware Framework
The photo IMG001 was taken at lat=S 3° 45' 48.6532“,
lon=W 38° 36' 28.7332“ on February 2nd, 2013 at 8:00AM. Address =Av. G, 162-229-Conj. Ceará,
Fortaleza – CE. Local Temperatura=29°C. Comment=
Dengue Habitat.
80%-90% likelihood of being withDengue (Local Context)
Information: lat=S 3° 45' 48.6429“, lon=W 38° 36' 28.7434“ on March 3rd, 2013 at 5:00PM. Body temperature=40°C, Heart rate=110 bpm,
Blood pressure=140/90. Address=Av. F, 126-298-Conj. Ceará, Fortaleza
– CE. Local Temperature=25°C. Symptoms=Headache, Vomiting, Body aches. Patient B
Patient A
Health Agent
Relocating a health agent for the Patient B’s house
Information: lat=S 2° 22' 32.9483“, lon=W 34° 33' 21.4657“ on March
24th, 2013 at 3:00PM. Body temperature=37°C, Heart rate=90 bpm,
Blood pressure=120/80. Address= Av. da Sé, n° 227, Conj. Palmeiras,
Fortaleza – CE. Local Temperature=32°C. Symptoms=Chills, Diarrhea.
Who is the patient? Are Data Structured?
Slide 22
<lat>3° 45' 48.6429"</lat>
<lon>38° 36' 28.7434"</lon>
<time>17:00</time>
<date>03/02/2013</date>
<loc_name>Av. F, 126-298-Conj. Ceará, Fortaleza - CE</loc_name>
<sus_id>209968974640021</sus_id>
<symptom>A, B, C</symptom>
<body_temp>40°C</body_temp>
<heart_rate>110bpm</heart_rate>
<blood_pressure>140/90</blood_pressure>
<lat=S 3° 45' 48.6429“>, <lon=W 38° 36' 28.7434“>, <time=17:00h>,
<date=03/02/2013>, <body_temp=40°C>, <heart_rate=110 bpm>,
<blood_press=140/90>, <loc_add=Av. F, 126-298-Conj. Ceará,
Fortaleza – CE>, <loc_weather=25°C>, <symptom=A, B, C>,
<sus_id=209968974640021>
metadata file
Building a metadata
file
Geolocation
Patient Identification
Health Information
Slide 23
Context Providers
Data Processing
Data Acquisition
Publishing
User
Device
Internet Health Agent
DeviceSymptoms + sus_id
Global
Context
Local
Context
Inference
Rules
Context Aggregator (CA)
Health Managers
System
Health Managers
System
Security Protocol
Metadata
LARIISA’s Architecture: a context-aware framework
Slide 24
IN
LARIISA_BAY Inference Module
Patient
Health Agent
Specialist
DecisionModule
Interface
Specialist DecisionModule
11Specialist Decision:
f(%)
33Pass
ThroughA �f(%)
A = A’
22Specialist
Validation:A �f(%)
A ≠ A’
RB
%
SITUATION ROOM
Health Agent
OUT
A’
B’
C’
A’
B’
C’
C
B
A
A
C
B
OTHER CONTEXT
PROVIDERS
A’
B’
C’
LARIISA
INFERENCE MODULE
Sensors
Health Center Ambulance
METADATA
User Interface
Inference
Rules
Global Context
Repository
Local Context
Repository
Patient Specialist
Epidemic Graph
Manager
LARIISA: Functional Diagram
Slide 25
5. Prototype
Slide 26
Data Acquisition(Patient CONTEXT)
DECISION-MAKING
LARIISA’s Prototype
METADATA
Option 1
Option 3
Option 2
LARIISA: A Context-Aware Framework
ONTOLOGY BAYESIAN NETWORKS
Slide 27
Local health context model
Global health context model
Prototype: Dengue Fever Case Study
Metadata
Slide 28
ENTRADA DO
SISTEMA
Módulo de Inferência do LARIISA_Bay
Paciente
Agente de Saúde
Especialista
Interface Módulo de
Decisão
Módulo de Decisão
11Decisão do
Especialista: f(%)
33Pass
ThroughA �f(%)
A = A’
22Validação do Especialista:
A �f(%)A ≠ A’
RB
%
SALA DE SITUAÇÃO
Agente de Saúde
SAÍDA DO SISTEMA
A’
B’
C’
A’
B’
C’
C
B
A
A
C
B
OUTROSPROVEDORES DE CONTEXTO
A’
B’
C’
LARIISA
LARIISA_Bay
Sensores
Posto de Saúde Ambulância
METADADO
Interface do Usuário
Regras de
Inferência
Repositório de
Contexto Global
Repositório de
Contexto Local
Paciente Especialista
Gráfico de Epidemias
Gestor
Screens of theproposed System
Patient
Health Agent
Specialist
Slide 29
Slide 30
Conclusion
Slide 31
Conclusão
Diga Saude (FUNCAP)
SISA (FIOCRUZ)
LARIISA (IFCE)
GISSA (FINEP)
Next Saude (DATASUS / Min Saúde)
Sponsors
LARIISA project is being sponsored since 2004 by
the Science and Technology Ministry of Brazil and others
Brazilian Research Agencies
It will be applied to the brazilian public health system
Slide 3215th Healthcom October 10th, 2013 Lisbon, Portugal Slide 32
Mauro [email protected]
+55 85 9705 4321
www.maurooliveira.com.br
Slide 33
MUITO OBRIGADO