1

Università degli Studi di SalernoDipartimento DISA-MIS

Laurea Magistrale in Tecnologie Informatiche e Management

FRIEND:A Framework for the Representation and

Identification of Diseases in Medical Records

CandidatoLuigi Vecchione

Matr : 0222500083

RelatoreProf. Giuseppe Polese

Anno Accademico - 2015/2016

2

CAN I DO IT?

3

Can I COPY & AUTOMATIZE that?

The Data AnalysisThe Problem The Diagnosis!!!

4

5

6

Problem Description

Agenda5 Steps to GO!

A brief description of the PROBLEM

and an introduction to the chosen

solution.

Background

All about the knowledge of the DOMAIN and the

actual STATE OF ART of the subject.

Framework

The detailed description of the problem solution.

The FRIEND FRAMEWORK.

Architecture

An high level understanding of the

framework’s ideal ARCHITECTURE.

Conclusions

The GIVEN RESULTS and the FUTURE

DEVELOPMENTS

7

PROBLEM DESCRIPTION

Wherever the art of medicine is loved, there is also a love of

HUMANITY! ”“

[Hippocrates]

8

25%

Overdiagnosis Errors in Treatment Lag Time Wrong Interpretation

Percentage of diagnostic error in Medicine 1

25% 15% 35%

1. Schiff GD et al. Diagnostic error in Medicine. Arch Intern Med 2009; 169: 1881-1887

9

Fast Delivery of Information

Framework Rules

10

Fast Delivery of Information

Easy To Understand

Framework Rules

11

Fast Delivery of Information

Easy To Understand

Case Oriented

Framework Rules

12

Fast Delivery of Information

Easy To Understand

Case Oriented

Information Guarantee

Framework Rules

13

BACKGROUND

14

Hybrid Background

Evidence Based Medicine

15

Hybrid Background

Evidence Based Medicine

Knowledge Representation

16

Evidence Based Medicine

Patient Values

Clinical DataResearch Evidence

OptimalDecision

17

Evidence Based Medicine

Patient Values

Clinical DataResearch Evidence

OptimalDecision

18

Evidence Based Medicine

Patient Values

Clinical DataResearch Evidence

OptimalDecision

19

Evidence Based Medicine

Patient Values

Clinical DataResearch Evidence

OptimalDecision

20

SEPSIS STEPS

21

• Temp > 38.0 C° or Temp < 36.8 C°• Respiratory Rate > 20/min• Heart Rate > 90/min• White Blood Cell > 12.000

< 4.000 > 10% bands

• PCO2 < 32mmHg

SIRS

SEPSIS STEPS

22

SEPSIS STEPS

• Temp > 38.0 C° or Temp < 36.8 C°• Respiratory Rate > 20/min• Heart Rate > 90/min• White Blood Cell > 12.000

< 4.000 > 10% bands

• PCO2 < 32mmHg

SEPSIS

SIRS 2 SIRS

+

Confirmed or Suspected Infection

23

• Temp > 38.0 C° or Temp < 36.8 C°• Respiratory Rate > 20/min• Heart Rate > 90/min• White Blood Cell > 12.000

< 4.000 > 10% bands

• PCO2 < 32mmHg

SEPSIS

Severe Sepsis

SIRS 2 SIRS

+

Confirmed or Suspected Infection

Sepsis

+

Signs of End Organ Damage

Hypotension

Lactate >4 mmol

SEPSIS STEPS

24

• Temp > 38.0 C° or Temp < 36.8 C°• Respiratory Rate > 20/min• Heart Rate > 90/min• White Blood Cell > 12.000

< 4.000 > 10% bands

• PCO2 < 32mmHg

SEPSIS

SEPTICSHOCK

Severe Sepsis

SIRS 2 SIRS

+

Confirmed or Suspected Infection

Sepsis

+

Signs of End Organ Damage

Hypotension

Lactate >4 mmol

Severe Sepsis with persistent:

+

Signs of End Organ Damage

Hypotension

Lactate >4 mmol

SEPSIS STEPS

25

NLP in Electronic Health Records

26

Knowledge Representation: Ontology

“An Ontology is a formal naming and definition of

the types, properties, and interrelationships of the entities that really or fundamentally exist for a

particular domain of discourse. ”

27

Knowledge Representation: Ontology

“An Ontology is a formal naming and definition of

the types, properties, and interrelationships of the entities that really or fundamentally exist for a

particular domain of discourse. ”

28

Medical Information Extraction trough NLP

Tested on Real Cases

Linguistic String Project (LGP)

Special Purpose RadiologySystem (SPRUS)

Medical Language Extraction Encoding system (MedLee)

Framework for the identification and representation of diseases

(FrIenD)

Multi CasesCoverage

Verificated & Validated

29

FRAMEWORK

30

INPUT : EHR

EXTRACTION OF DATA

TROUGH AN

EXTRACTOR BASED ON

NLP & REGEX.

Overview

31

INPUT : EHR Entity Representation

EXTRACTION OF DATA

TROUGH AN

EXTRACTOR BASED ON

NLP & REGEX.

DEFININING AND

SAVING OF

EXTRACTED DATA

INTO THE DEDICATED

FRAMES.

Overview

32

INPUT : EHR Entity Representation Define & Modelling Correlations

EXTRACTION OF DATA

TROUGH AN

EXTRACTOR BASED ON

NLP & REGEX.

DEFININING AND

SAVING OF

EXTRACTED DATA

INTO THE DEDICATED

FRAMES.

DEFINING &

MODELLING OF

CORRELATIONS

BETWEEN

CONCEPTS.

Overview

33

INPUT : EHR Entity Representation Define & Modelling Correlations

OntologyImplementation

EXTRACTION OF DATA

TROUGH AN

EXTRACTOR BASED ON

NLP & REGEX.

DEFININING AND

SAVING OF

EXTRACTED DATA

INTO THE DEDICATED

FRAMES.

DEFINING &

MODELLING OF

CORRELATIONS

BETWEEN

CONCEPTS.

IMPLEMENTATION

OF AN ONTOLOGY

BASED ON THE

GIVEN ENTITY AND

THEM PROPS.

Filtering & Understanding

Overview

34

INPUT : EHR Entity Representation Define & Modelling Correlations

OntologyImplementation

EXTRACTION OF DATA

TROUGH AN

EXTRACTOR BASED ON

NLP & REGEX.

DEFININING AND

SAVING OF

EXTRACTED DATA

INTO THE DEDICATED

FRAMES.

DEFINING &

MODELLING OF

CORRELATIONS

BETWEEN

CONCEPTS.

IMPLEMENTATION

OF AN ONTOLOGY

BASED ON THE

GIVEN ENTITY AND

THEM PROPS.

FILTERING OF THE

ONTOLOGY

TROUGH SPARQL

QUERIES.

Overview

Filtering

35

INPUT : EHR Entity Representation Define & Modelling Correlations

OntologyImplementation

EXTRACTION OF DATA

TROUGH AN

EXTRACTOR BASED ON

NLP & REGEX.

DEFININING AND

SAVING OF

EXTRACTED DATA

INTO THE DEDICATED

FRAMES.

DEFINING &

MODELLING OF

CORRELATIONS

BETWEEN

CONCEPTS.

IMPLEMENTATION

OF AN ONTOLOGY

BASED ON THE

GIVEN ENTITY AND

THEM PROPS.

UNDERSTANDING

OF THE GIVEN

RESULTS.

SENDING OF

ALERT FOR

SEPSIS

Filtering ALERT SENDING

Overview

FILTERING OF THE

ONTOLOGY

TROUGH SPARQL

QUERIES.

36

INPUT : EHR

EXTRACTION OF DATA

TROUGH AN

EXTRACTOR BASED ON

NLP & REGEX.

First Phase

37

38

Segmentation

39

Keyword Identification

40

Noise Removal

41

dateTime Tracking

42

INPUT : EHR

Second Phase

Entity Representation

DEFININING AND

SAVING OF

EXTRACTED DATA

INTO THE DEDICATED

FRAMES.

43

Standard Entity Frame

44

Keyword: Paziente

45

Third Phase

Define & Modelling Correlations

DEFINING &

MODELLING OF

CORRELATIONS

BETWEEN

CONCEPTS.

46

Modelling Correlations

Related

Related

Related

47

Fourth Phase

Define & Modelling Correlations

OntologyImplementation

IMPLEMENTATION

OF AN ONTOLOGY

BASED ON THE

GIVEN ENTITY AND

THEM PROPS.

48

Ontology Implementation

49

Individuals

50

dateTime Property

51

Ontology Correlations

52

Complete Ontology

53

Fifth Phase

FILTERING OF THE

ONTOLOGY

TROUGH SPARQL

QUERIES.

Filtering

54

Filtering

SELECT * WHERE { ?s :date ?date. FILTER (?date ="2016-03-09T13:08:00"^^xsd:dateTime)}

SPARQL

55

Filtering

SELECT * WHERE { ?s :date ?date. FILTER (?date ="2016-03-09T13:08:00"^^xsd:dateTime)}

SPARQL

56

Final Phase

ALERT SENDING

UNDERSTANDING

OF THE GIVEN

RESULTS.

SENDING OF

ALERT FOR

SEPSIS

57

Understanding Model : SIRS CRITERIA

58

4 OUT OF 6 SEPSIS ALERT

59

ARCHITECTURE

60

High-Level Overview

61

CONCLUSION

62

63

64

65

66

67

68

TEST ON A DIFFERENT DIAGNOSIS

69

TEST ON A DIFFERENT DIAGNOSIS

GRAPHICAL IMPROVEMENT OF THE MODEL

70

TEST ON A DIFFERENT DIAGNOSIS

STRUCTURING OF DATATROUGH SPARQL

GRAPHICAL IMPROVEMENT OF THE MODEL

WHAT WHY WHERE WHEN WHO HOW

THANKS FOR COMING!!!