presentazione tesi di laurea magistrale
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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
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CAN I DO IT?
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Can I COPY & AUTOMATIZE that?
The Data AnalysisThe Problem The Diagnosis!!!
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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
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PROBLEM DESCRIPTION
Wherever the art of medicine is loved, there is also a love of
HUMANITY! ”“
[Hippocrates]
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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
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Fast Delivery of Information
Framework Rules
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Fast Delivery of Information
Easy To Understand
Framework Rules
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Fast Delivery of Information
Easy To Understand
Case Oriented
Framework Rules
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Fast Delivery of Information
Easy To Understand
Case Oriented
Information Guarantee
Framework Rules
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BACKGROUND
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Hybrid Background
Evidence Based Medicine
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Hybrid Background
Evidence Based Medicine
Knowledge Representation
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Evidence Based Medicine
Patient Values
Clinical DataResearch Evidence
OptimalDecision
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Evidence Based Medicine
Patient Values
Clinical DataResearch Evidence
OptimalDecision
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Evidence Based Medicine
Patient Values
Clinical DataResearch Evidence
OptimalDecision
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Evidence Based Medicine
Patient Values
Clinical DataResearch Evidence
OptimalDecision
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SEPSIS STEPS
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• 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
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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
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• 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
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• 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
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NLP in Electronic Health Records
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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. ”
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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. ”
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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
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FRAMEWORK
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INPUT : EHR
EXTRACTION OF DATA
TROUGH AN
EXTRACTOR BASED ON
NLP & REGEX.
Overview
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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
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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
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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
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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
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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.
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INPUT : EHR
EXTRACTION OF DATA
TROUGH AN
EXTRACTOR BASED ON
NLP & REGEX.
First Phase
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Segmentation
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Keyword Identification
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Noise Removal
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dateTime Tracking
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INPUT : EHR
Second Phase
Entity Representation
DEFININING AND
SAVING OF
EXTRACTED DATA
INTO THE DEDICATED
FRAMES.
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Standard Entity Frame
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Keyword: Paziente
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Third Phase
Define & Modelling Correlations
DEFINING &
MODELLING OF
CORRELATIONS
BETWEEN
CONCEPTS.
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Modelling Correlations
Related
Related
Related
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Fourth Phase
Define & Modelling Correlations
OntologyImplementation
IMPLEMENTATION
OF AN ONTOLOGY
BASED ON THE
GIVEN ENTITY AND
THEM PROPS.
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Ontology Implementation
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Individuals
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dateTime Property
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Ontology Correlations
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Complete Ontology
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Fifth Phase
FILTERING OF THE
ONTOLOGY
TROUGH SPARQL
QUERIES.
Filtering
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Filtering
SELECT * WHERE { ?s :date ?date. FILTER (?date ="2016-03-09T13:08:00"^^xsd:dateTime)}
SPARQL
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Filtering
SELECT * WHERE { ?s :date ?date. FILTER (?date ="2016-03-09T13:08:00"^^xsd:dateTime)}
SPARQL
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Final Phase
ALERT SENDING
UNDERSTANDING
OF THE GIVEN
RESULTS.
SENDING OF
ALERT FOR
SEPSIS
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Understanding Model : SIRS CRITERIA
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4 OUT OF 6 SEPSIS ALERT
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ARCHITECTURE
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High-Level Overview
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CONCLUSION
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TEST ON A DIFFERENT DIAGNOSIS
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TEST ON A DIFFERENT DIAGNOSIS
GRAPHICAL IMPROVEMENT OF THE MODEL
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TEST ON A DIFFERENT DIAGNOSIS
STRUCTURING OF DATATROUGH SPARQL
GRAPHICAL IMPROVEMENT OF THE MODEL
WHAT WHY WHERE WHEN WHO HOW
THANKS FOR COMING!!!