1. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 1 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 1 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 MPCOMP Master Program on Computer Science Ceará - Brasil MPCOMP

2. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 2 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 2 Leonardo Gardini State University of Ceara (UECE) Fortaleza, Brazil lgardini@gmail.com Germanno Teles Federal University of Ceara (UFC) Fortaleza, Brazil gemanno@gmail.com Ronaldo Ramos Federal Institute of Ceará (IFCE) Fortaleza, Brazil ronaldoramos@gmail.com Cesar Moura Federal Institute of Ceará (IFCE) Fortaleza, Brazil cesar@edu.ifce.br Reinaldo Braga Federal Institute of Ceará (IFCE) Aracati, Brazil reinaldobraga@gmail.com José Bringel Federal University of Ceara (UFC) Fortaleza, Brazil bringelfilho@gmail.com Odorico Andrade Federal University of Ceara (UFC) Fortaleza, Brazil odorico0811@gmail.com Mauro Oliveira Federal Institute of Ceará (IFCE) Aracati, Brazil amauroboliveira@gmail.com EVOLVING AN INTELLIGENT FRAMEWORK FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS EVOLVING AN INTELLIGENT FRAMEWORK FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS Team working on this project

3. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 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. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 4 1. Contextualization 2. LARIISA Solution (2010) 3. Ontology and Health Application 4. LARIISA Architecture 5. LARIISA Prototypes - Dengue fever study case (2012) - A Bayesian Approach (2013) - Caregiving (2014) - Car Aciddent (2015) 6. LARIISA Next Generation - An Intelligent Classifier for LARIISA - LARIISA Cube Architecture 7. Conclusion Summary

5. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 5 1. Contextualization

6. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 6 Health System’s Evolution Cost ¢ Fonte: K. Jennings, K. Miller, S. Materna (1997) $ Industrial Era Information Era Disease PreventionDisease Treatment Cost ¢ $ HOSPITALHOMECARE

7. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 7 Health System - Information Era Based on Disease PREVENTION Costs ¢ Encouraged Discouraged Fonte: K. Jennings, K. Miller, S. Materna (1997) Hospital (specialits) Health Agent Primary Health Care $ CONTEXTUALIZATION Descentralization of Public Health System

8. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 8 PROBLEM Primary Health Care Primary Health Care Hospital (specialits) Hospital (specialits) Health Agent Health Agent Management Information Message Data Acquisition DESCENTRALIZATION  Increasing Complexity of Health Management Increasing Complexity of Health Management

9. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 9 2. The LARIISA Solution

10. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 10 The objective of the LARIISA is to provide a software platform that allows the decision-making model for public health system. Real Time Information Real Time Information Health Knowledge Inference Mechanism Inference Mechanism Decision-making Application Decision-making Application GENESIS of the LARIISA Project Context-aware Technology

11. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 11 CONTEXT-AWARE FRAMEWORK SOLUTION ONTOLOGY Knowledge Representation Metadata Geolocation Decision- Making Information Health Knowledge Inference Mechanism PROBLEM  DESCENTRALIZATION  LARIISA: an Intelligent System to support decision-making process

12. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 12 LARIISA, A Context-Aware Framework for Health Care Governance Decision-Making Systems: A model based on the Brazilian Digital TV Mauro Oliveira & Odorico Monteiro

13. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 13 INTELLIGENCE FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS INTELLIGENCE FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS Knowledge Management Knowledge Management Normative Clinical Epidemiology Administration Shared Information Shared Information

14. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 14 3. Ontology and Health Application

15. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 15 Semantic INTEROPERABILITY Problem REPRESENTING INFORMATION REAL 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 Why do we need Ontologies in healthcare Aplication?

16. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 16 PROBLEM: Data integration with diferent standards – AHA/MIT-BIH (Physionet) – SCP-ECG (ISO/DIS 11073-91064) – HL7 aECG Why do we need Ontologies in healthcare Aplication ? ELECTROCARDIOGRAM

17. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 17 AHA/MIT -BIH SCP-ECG HL7 aECG Ontology Why do we need Ontologies in healthcare Aplication?

18. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 18 Standard 1 Standard 2 Standard 3 Standard 4 Reference Model Why do we need Ontologies in healthcare Aplication?

19. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 19 Semantic Distance Conceptualization Interpretation Tan Tan Tan TAN...

20. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 20 Semantic Distance Conceitualization Interpretation

21. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 21 LARIISA Local health context model Prague, Czech Republic July/2013

22. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 22 LARIISA Global health context model Prague, Czech Republic July/2013

23. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 23 4. LARIISA Architecture

24. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 24 Knowledge Representation (ONTOLOGY) Data Acquisition (CONTEXT) DECISION-MAKING LARIISA’s Scenario METADATA

25. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 25 Context Providers Data Processing Data Acquisition Publishing User Device Internet Health Agent Device Symptoms + sus_id Global Context Local Context Inference Rules Context Aggregator (CA) Health Managers System Security Protocol Metadata LARIISA Architecture: a context-aware framework

26. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 26 Humidity Sensor Atmospheric Pressure Sensor Temperature Sensor Digital Camera Global Positioning System (GPS) Accelerometer Internet Connection Light Sensor Proximity Sensor Compass Gyroscope Geographical Information System (GIS)...many device sensors to explore and collect information from users... LARIISA proposes a Context-Aware Web Content Generator Based on Personal Tracking

27. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 27 NextSAUDE Fortaleza – Ceara - Brazil2013 Humidity Sensor Atmospheric Pressure Sensor Temperature Sensor Digital Camera Global Positioning System (GPS) Accelerometer Internet Connection Light Sensor Proximity Sensor Compass Gyroscope Medical Sensors Geographical Information System (GIS)

28. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 28 NextSAUDE Fortaleza – Ceara - Brazil2013 0023821992 3° 45' 48.6429" 38° 36' 28.7434" 17:00 03/02/2013 Av. F, 126-298-Conj. Ceará, Fortaleza - CE 209968974640021 A, B, C 40°C 110bpm 140/90 The diagnosis was taken at,, on at. Heart rate was, body temperature was and blood pressure was. Symptoms:. SUS ID:. metadata file LARIISA META DATA LARIISA META DATA Personal Tracking Patient Identification!! via Web Service Diagnosis data

29. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 29

30. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 30 Knowledge Representation (ONTOLOGY) Data Acquisition (CONTEXT) DECISION-MAKING LARIISA’s Scenario METADATA Step 1 Step 3 Step 2 LARIISA: A Context-aware Framework Based on Ontology Technology LARIISA: A Context-aware Framework Based on Ontology Technology

31. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 31 5. LARIISA Prototypes

32. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 32 5. LARIISA Prototypes - Dengue fever study case (2012)

33. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 33 Knowledge Representation (ONTOLOGY) Data Acquisition (CONTEXT) DECISION-MAKING LARIISA: Dengue Fever Case Study METADATA Step 1 Step 3 Step 2

34. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 34 Local health context model Prague, Czech Republic July/2013

35. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 35 Global health context model Prague, Czech Republic July/2013

36. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 36 Local health context model Global health context model LARIISA: Dengue Fever Case Study Metadata

37. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 37 Health Agent Pacient Smartphone / Tablet / Desktop,,,,,,,,,, Lariisa Database WEB Prototype Health Managers Dashboard Structured Data Internet

38. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 38 5. LARIISA Prototypes - A Bayesian Approach (2013)

39. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 39 Probabilistc Methods (BAYESIAN NETWORK) Data Acquisition (CONTEXT) DECISION-MAKING METADATA LARIISA: Bayesian Approach

40. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 40 LARIISA: Bayesian Approach Specialist Data (Tables) and Relationship

41. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 41 ENTRADA DO SISTEMA Módulo de Inferência do LARIISA_Bay Paciente Agente de Saúde Especialista Interface Módulo de Decisão Módulo de Decisão 1 1 Decisão do Especialista: f(%) 3 3 Pass Through A  f(%) A = A’ 2 2 Validação 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 OUTROS PROVEDORES 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 the proposed System Patient Health Agent Specialist

42. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 42 Low Risk High Risk Bayesian Networks

43. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 43 IN LARIISA_BAY Inference Module Patient Health Agent Specialist Decision Module Interface Specialist Decision Module 1 1 Specialist Decision: f(%) 3 3 Pass Through A  f(%) A = A’ 2 2 Specialist 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

44. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 44 5. LARIISA Prototypes - Caregiving (2014)

45. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 45 Knowledge Representation (ONTOLOGY) Data Acquisition (CONTEXT) DECISION-MAKING LARIISA: Caregiver Case Study METADATA

46. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 46 Scenario for LARIISA Application CAREGIVER – 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... or voluntary people

47. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 47 Prague, Czech Republic July/2013 Data acquisition – Diga Saude CONTEXT

48. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 48

49. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 49 Prague, Czech Republic July/2013 Interface for non alphabetized people

50. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 50 5. LARIISA Prototypes - Car Aciddent (2015)

51. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 51 Knowledge Representation (ONTOLOGY) Data Acquisition (CONTEXT) DECISION-MAKING LARIISA: Dengue Fever Case Study METADATA Step 1 Step 3 Step 2

52. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 52 When the smartphone feels a tremble, a screen will indicate that a countdown of thirty seconds has started. In parallel, a message informs you that the application is looking for the coordinates of the crash site. On this same screen, the option to disable is shown. 5Car Accident Interface

53. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 53 Health Agent Combining geolocation with opportunistic networks, we adopt a mechanism that searches for landmarks around the accident.

54. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 54 ONTOLOGY & BAYESIAN NETWORKS Data Acquisition (CONTEXT) METADATA DECISION-MAKING LARIISA: URGENCY CARE IN CAR ACCIDENTS

55. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 55 6. LARIISA Next Generation - An Intelligent Classifier for LARIISA - LARIISA Cube

56. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 56 6. LARIISA Next Generation - An Intelligent Classifier for LARIISA

57. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 57 ONTOLOGY BAYESIAN NETWORKS DATA MINING Data Acquisition (CONTEXT) METADATA DECISION-MAKING LARIISA: Next Generation

58. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 58 Clariisa Evolution - Classifier Data Acquisition Web Application Metadata Health diagnosis database Raw data extraction Feature selection Pattern building Predictive labeling Target patterns Non-target patterns Pattern 1 Pattern 2 Pattern n... Training set Undersample

59. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 59 Step 1: Extract raw data from the database, which contains usage information of the beneficiaries; Step 2: Perform feature selection; Step 3: Find every patient that did a health diagnosis somewhere in the analyzed period of time; NEW METHODOLOGY Used for building the test and training sets to be submitted to the classifiers. Find an optimal model to predict the development of heart diseases based on this type of data.

60. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 60 Step 5:Find every patient that have never had a disease during the same time period; Step 6:For each patient found on Step 5, build patterns considering a procedure window counted from each available month. Note that each reference month generates a distinct pattern per patient. (non-target patterns); Step 7:Obtain a training set formed by every target pattern and the same quantity of nontarget patterns, randomly sampled. Step 4: For each patient’s diagnosis data found on Step 3, build a pattern considering a procedure window counted from the first time a target diagnosis was done (target patterns);

61. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 61 Clariisa Evolution - Classifier Active health data (per SUS_ID) Set of test patterns Classifier mechanism Intelligent system choosen (bayesian networks or ontology) based on classifier mechanism Health diagnosis provided through the best intelligent system choosen by the prototype Target patients Non-target patients

62. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 62 6. LARIISA Next Generation - LARIISA Cube

63. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 63 - LARIISA Cube

64. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 64 - LARIISA Cube

65. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 65 Context Providers Data Processing Data Acquisition Publishing User Device Internet Health Agent Device Symptoms + sus_id Global Context Local Context Inference Rules Context Aggregator (CA) Health Managers System Security Protocol Metadata LARIISA Architecture: a context-aware framework

66. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 66 INTELLIGENCE FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS INTELLIGENCE FOR DECISION- MAKING PROCESS IN E-HEALTH SYSTEMS Knowledge Management Knowledge Management Normative Clinical Epidemiology Administration Shared Information Shared Information

67. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 67 Privacy Policy Manager (PPM) Systemic Normative Clinical & Epidemiology Administrative Shared Management Context-Aware Service (CAS)

68. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 68

69. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 69 Health Agent and Users`s Mobile Device (MD) Context Provider (CP) S S S S S S Embedded Sensors Local Context Repository Context Provider (CP) S S S S S S Environmental Sensors Set Top Box S S S S S S Helath Sensors Privacy Policies Privacy Policy Manager (PPM) Systemic Normative Collect Storage Integration Processing Clinical & Epidemiology Administrative Shared Management Health Context Information Health Agent`s Agenda Health Agent`s Database Metadata (MTD) Context Reasoner (CR)Context Aggregator (CA) Context-Aware Service (CAS) Inference Rules Knowledge Management (KTA) Artificial Intelligence Service Adapter (SA) Visualization Query Adapter (QA) QoC Evaluator (QoCE) Global Context Repository Bayesian Networks Ontology Linked Data Mashups Geolocation Classifiers

70. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 70 7. Conclusion

71. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 71 Conclusão 2006 - Diga Saude (FUNCAP) 2010 - LARIISA (CNPq / CAPES) 2012 - Cloud LARIISA (IFCE) 2013 - SISA (FIOCRUZ) 2014– NextSAUDE (FUNCAP) 2015 - GISSA (FINEP) Sponsors This 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 (SUS) This 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 (SUS) CNPq

72. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 72 The CLARIISA project will be applied to the SUS, The brazilian public health system ACADEMIC RESULTS 10 Master of Science 12 papers 03 research projects INOVATION ASPECTS A Proof of Concept of the LARIISA Applications is being implemented by the IT companies in Brazil The DIGA Saúde prototype is held by the COELCE (Electricity Company of Ceará State – Brazil) The CLARIISA project will be applied to the SUS, The brazilian public health system ACADEMIC RESULTS 10 Master of Science 12 papers 03 research projects INOVATION ASPECTS A Proof of Concept of the LARIISA Applications is being implemented by the IT companies in Brazil The DIGA Saúde prototype is held by the COELCE (Electricity Company of Ceará State – Brazil)

73. 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 73 15th Healthcom October 10th, 2013 Lisbon, Portugal Slide 73