1 / 73

EVOLVING AN INTELLIGENT FRAMEWORK FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS

MPCOMP. MPCOMP Master Program on Computer Science Ceará - Brasil. EVOLVING AN INTELLIGENT FRAMEWORK FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS. MAURO OLIVEIRA www.maurooliveira.com.br. LAR-A Computer Network Laboratory of Aracati. Team working on this project.

keona
Télécharger la présentation

EVOLVING AN INTELLIGENT FRAMEWORK FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MPCOMP MPCOMP Master Programon Computer Science Ceará - Brasil EVOLVING AN INTELLIGENT FRAMEWORK FORDECISION- MAKING PROCESS IN E-HEALTH SYSTEMS MAURO OLIVEIRA www.maurooliveira.com.br LAR-A Computer Network Laboratoryof Aracati

  2. Team workingonthis project EVOLVING AN INTELLIGENT FRAMEWORK FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS

  3. About this lecture … LARIISA is an intelligent framework for enhancing the decision making process on the health public system. • This lecture presents the evolution of LARIISA towards an intelligent classifier model and the new Cube architecture.

  4. Summary 1. Contextualization 2. LARIISA Solution (2010) 3. Ontologyand Health Application 4. LARIISA Architecture 5. LARIISA Prototypes - Dengue feverstudy case (2012) - A Bayesian Approach (2013) - Caregiving (2014) - CarAciddent (2015) 6. LARIISA Next Generation - AnIntelligentClassifier for LARIISA - LARIISA Cube Architecture 7. Conclusion

  5. 1. Contextualization

  6. Health System’sEvolution Fonte: K. Jennings, K. Miller, S. Materna (1997) Industrial Era InformationEra Cost Cost ¢ $ $ ¢ DiseaseTreatment DiseasePrevention HOSPITAL HOMECARE

  7. CONTEXTUALIZATION Health System - Information Era BasedonDisease PREVENTION Encouraged Costs ¢ Primary Health Care DescentralizationofPublic Health System Health Agent Hospital (specialits) $ Discouraged Fonte: K. Jennings, K. Miller, S. Materna (1997)

  8. PROBLEM DESCENTRALIZATION  Hospital (specialits) Message Management IncreasingComplexityof Health Management Information Health Agent Primary Health Care Data Acquisition

  9. 2. The LARIISA Solution

  10. GENESIS oftheLARIISA Project The objectiveoftheLARIISAistoprovide a software platformthatallowsthe decision-making model for public health system. Decision-making Application Inference Mechanism Health Knowledge Context-aware Technology Real Time Information

  11. SOLUTION DESCENTRALIZATION  PROBLEM  CONTEXT-AWARE FRAMEWORK Decision-Making ONTOLOGY Knowledge Representation Information InferenceMechanism LARIISA: anIntelligent System tosupportdecision-makingprocess Health Knowledge Geolocation Metadata

  12. LARIISA, A Context-Aware Framework for Health Care Governance Decision-Making Systems: A model based on the Brazilian Digital TV Mauro Oliveira & OdoricoMonteiro

  13. Knowledge Management INTELLIGENCE FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS Shared Information Normative Administration ClinicalEpidemiology

  14. 3. Ontologyand Health Application

  15. Why do weneedOntologiesin healthcareAplication? REPRESENTING INFORMATION • An ontology is a "formal, explicit specification of a shared conceptualisation". • It formally represents knowledge as a set of concepts within a domain, and the relationships between theses concepts. • It can be used to model a domain and support reasoning about concepts REAL INFORMATION Semantic INTEROPERABILITY Problem

  16. Why do weneedOntologiesin healthcareAplication? • PROBLEM: Data integrationwithdiferent standards • AHA/MIT-BIH (Physionet) • SCP-ECG (ISO/DIS 11073-91064) • HL7 aECG ELECTROCARDIOGRAM

  17. Why do weneedOntologiesin healthcareAplication? Ontology AHA/MIT-BIH SCP-ECG HL7 aECG

  18. Why do weneedOntologiesin healthcareAplication? Reference Model Standard4 Standard1 Standard2 Standard3

  19. SemanticDistance Conceptualization Interpretation TanTanTan TAN...

  20. SemanticDistance Conceitualization Interpretation

  21. LARIISA Local health context model Prague, Czech Republic July/2013

  22. LARIISA Global health context model Prague, Czech Republic July/2013

  23. 4. LARIISA Architecture

  24. LARIISA’sScenario KnowledgeRepresentation (ONTOLOGY) Data Acquisition (CONTEXT) METADATA DECISION-MAKING

  25. LARIISA Architecture: a context-aware framework Data Acquisition ContextProviders Health Agent Device Internet Symptoms + sus_id User Device Data Processing Metadata Publishing Security Protocol ContextAggregator (CA) Health Managers System InferenceRules Global Context Local Context

  26. LARIISAproposes aContext-Aware Web Content Generator Based on Personal Tracking Humidity Sensor Temperature Sensor AtmosphericPressure Sensor Accelerometer Global Positioning System (GPS) Internet Connection Compass Digital Camera Light Sensor GeographicalInformation System (GIS) Proximity Sensor Gyroscope ...manydevicesensorsto explore andcollectinformationfromusers...

  27. Digital Camera Global Positioning System (GPS) Internet Connection Proximity Sensor Compass Gyroscope Accelerometer Light Sensor Temperature Sensor Humidity Sensor GeographicalInformation System (GIS) AtmosphericPressure Sensor Medical Sensors

  28. The diagnosis <0023821992> was taken at <S 3° 45' 48.6429“>, <W 38° 36' 28.7434“>, <Av. F, 126-298-Conj. Ceará, Fortaleza – CE> on <March 2nd, 2013> at <17:00h>. Heart rate was <110 bpm>, body temperature was <40°C> and blood pressure was <140/90>. Symptoms: <A, B, C>. SUS ID: <209968974640021>. LARIISA META DATA <diagnosis_id>0023821992</diagnosis_id> <lat>3° 45' 48.6429"</lat> PersonalTracking <lon>38° 36' 28.7434"</lon> <time>17:00</time> via Web Service <date>03/02/2013</date> <body_temp>40°C</body_temp> <heart_rate>110bpm</heart_rate> Diagnosis data <blood_pressure>140/90</blood_pressure> metadata file <loc_name>Av. F, 126-298-Conj. Ceará, Fortaleza - CE</loc_name> <symptom>A, B, C</symptom> PatientIdentification!! <sus_id>209968974640021</sus_id>

  29. LARIISA’sScenario KnowledgeRepresentation (ONTOLOGY) Data Acquisition (CONTEXT) METADATA Step 1 LARIISA: A Context-aware Framework BasedonOntology Technology Step 2 DECISION-MAKING Step 3

  30. 5. LARIISA Prototypes

  31. 5. LARIISA Prototypes - Dengue feverstudy case (2012)

  32. LARIISA: Dengue Fever Case Study KnowledgeRepresentation (ONTOLOGY) Data Acquisition (CONTEXT) METADATA Step 1 Step 2 DECISION-MAKING Step 3

  33. Local health context model Prague, Czech Republic July/2013

  34. Global health context model Prague, Czech Republic July/2013

  35. LARIISA: Dengue Fever Case Study Local health context model Metadata Global health context model

  36. WEB Prototype Structured Data <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> Internet Health Agent LariisaDatabase Smartphone / Tablet / Desktop Health Managers Dashboard Pacient

  37. 5. LARIISA Prototypes - A Bayesian Approach (2013)

  38. LARIISA: Bayesian Approach ProbabilistcMethods (BAYESIAN NETWORK) Data Acquisition (CONTEXT) METADATA DECISION-MAKING

  39. LARIISA: Bayesian Approach Specialist Data (Tables) andRelationship

  40. Screensoftheproposed System ENTRADA DO SISTEMA SALA DE SITUAÇÃO SAÍDA DO SISTEMA Módulo de Decisão Interface Módulo de Decisão A’ Interface do Usuário 1 B’ Módulo de Inferência do LARIISA_Bay C’ Decisão do Especialista: f(%) RB A’ A B B’ Gestor 2 C’ Patient C Agente de Saúde Health Agent Validação do Especialista: A f(%) A ≠ A’ Paciente Paciente % A’ Posto de Saúde A Agente de Saúde Specialist B B’ Gráfico de Epidemias Sensores C’ C Ambulância METADADO 3 Especialista Especialista Pass Through A f(%) A = A’ LARIISA_Bay OUTROS PROVEDORES DE CONTEXTO Regras de Inferência Repositório de Contexto Global Repositório de Contexto Local LARIISA

  41. Bayesian Networks High Risk LowRisk

  42. LARIISA: FunctionalDiagram INFERENCE MODULE SITUATION ROOM IN OUT 1 SpecialistDecision Module Decision Module Interface A’ User Interface Specialist Decision: f(%) LARIISA_BAY InferenceModule B’ RB C’ Manager A’ A 2 B B’ Health Agent C’ C Specialist Validation: A f(%) A ≠ A’ Patient Patient % Health Center Health Agent A’ A EpidemicGraph B B’ Sensors C’ C Ambulance METADATA 3 Specialist Specialist Pass Through A f(%) A = A’ OTHER CONTEXT PROVIDERS LARIISA InferenceRules Global ContextRepository Local ContextRepository

  43. 5. LARIISA Prototypes - Caregiving (2014)

  44. LARIISA: CaregiverCase Study KnowledgeRepresentation (ONTOLOGY) Data Acquisition (CONTEXT) METADATA DECISION-MAKING

  45. Scenario for LARIISA ApplicationCAREGIVER – a voluntary or paid person who helps an impaired person to deal with his/her daily activities. 2. Motivation: The Brazilian Digital TV LARIISA can help specialized profissional ... orvoluntarypeople

  46. Data acquisition – DigaSaude CONTEXT Prague, Czech Republic July/2013

  47. Interface for non alphabetized people Prague, Czech Republic July/2013

  48. 5. LARIISA Prototypes - CarAciddent (2015)

More Related