Decision support and image signal analysis in heart failure a comprehensive use case
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Decision Support and Image & Signal Analysis in Heart Failure A Comprehensive Use Case. Foreword. This research work is being carried out within the European STREP project HEARTFAID Thematic Priority : Information Society Technology – ICT for Health Instrument: STREP

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Foreword l.jpg
Foreword Failure

This research work is being carried out within the European STREP project HEARTFAID

  • Thematic Priority: Information Society Technology – ICT for Health

  • Instrument: STREP

  • Project Identifier: IST-2005-027107

  • Time-table: 2006-2009

  • Project web site:www.heartfaid.org

    Consortium

Knowledge Discovery and Decision Support Systems in Health Information Systems


Outline l.jpg
Outline Failure

HEARTFAID Project

HEARTFAID Decision Support and Data Processing Services

A Significant Scenario

Current Results

Conclusions & Future Activities

Knowledge Discovery and Decision Support Systems in Health Information Systems


Heartfaid project l.jpg

HEARTFAID Project Failure

HEARTFAID Project

  • A KNOWLEDGE BASED PLATFORM OF SERVICES

  • for supporting medical-clinical management of HEART FAILURE (HF) within elderly population

  • By devising:

  • an innovative technological platform for informative and decision support based on an umbrella of services

  • defining new health care delivery organization integrating different health care environments and operators for a patient centric management program

Knowledge Discovery and Decision Support Systems in Health Information Systems


Heartfaid platform of services l.jpg

HEARTFAID Project Failure

HEARTFAID Platform of Services

  • Data Collection & Management

    • integration of heterogeneous data from biomedical devices, clinical reports, telemonitoring

  • Knowledge-based Decision Support

    • supporting the HF health care operators in health care personalization of the HF patients

  • End-user Applications

    • doorway to a multitude of end-user utilities and services, such as accessing an electronic health record, querying the clinical decision support system

Data Collection and

Management

End-user Applications

Knowledge-based Decision Support

Knowledge Discovery and Decision Support Systems in Health Information Systems


Core heartfaid platform components l.jpg

HEARTFAID Project Failure

Core HEARTFAID Platform Components

Thecoreof HEARTFAIDintelligence

  • A Knowledge base for the relevant medical domain

  • Innovative inference engines methodologies for medical decision support

  • Innovative approaches for biomedical signal and image processing

Brain

Heart

HEARTFAIDClinical Decision Support System (CDSS)

Knowledge Discovery and Decision Support Systems in Health Information Systems


Heartfaid decision support and data processing services l.jpg

HEARTFAID Decision Support and Data Processing Services Failure

HEARTFAID Decision Support and Data Processing Services

Accurate design activities

  • Signals and Images Processing:

    • Diagnostic resources investigation

    • Data processing relevance in routine and research workflows

  • Decision Support:

    • Methodological foundations and technological State of the Art analysis

    • Heart Failure Domain investigation

Knowledge Discovery and Decision Support Systems in Health Information Systems


Data processing resource investigation l.jpg

HEARTFAID Decision Support and Data Processing Services Failure

Data Processing: Resource Investigation

1D Signals

Holter

ECG

Exercise

  • Diagnostic Resource Significance

  • Representation Features

Echo

Chest X-ray

2D Images

MRI

Nuclear

3D Images

Knowledge Discovery and Decision Support Systems in Health Information Systems


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HEARTFAID Decision Support and Data Processing Services Failure

Data Processing: Routine vs. Innovation

Routine Clinical Practice – HFP Level 2

  • Fill instrumentation lacks

  • Reduce analysis subjectivity

Reduceintra/inter-observer variability

Open-problems

Assessment

Long -Term Research Environment – HFP Level 4

Correlation Analysis

Signal/Image Categorization

  • Extend HF knowledge

Extractinnovative representing features

Knowledge Discovery and Decision Support Systems in Health Information Systems


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HEARTFAID Decision Support and Data Processing Services Failure

Decision Support: Methodological Foundations Analysis

DSS

KB-DSS

CDSS

HEARTFAID

CDSS

Knowledge Discovery and Decision Support Systems in Health Information Systems


Heart failure domain investigation l.jpg

HEARTFAID Decision Support and Data Processing Services Failure

Heart Failure Domain Investigation

  • Analysis of the medical domain for individuating the decision problemsthat require HEARTFAID CDSS intervention

Specific Problems

  • Diagnosis:

    • Assessment and severity evaluation of heart failure

    • Analysis of diagnostic exams

  • Prognosis

    • Prognosis stratification

  • Therapy:

    • Identification of suitable pathways

    • Planning of adequate, patient’s specific therapy

  • Follow-up:

    • Suggestion of changes in management and treatment

    • Early detection of patient’s decompensation

HEARTFAID Problem Domains

  • Diagnosis

  • Prognosis

  • Therapy

  • Follow-up

Knowledge Discovery and Decision Support Systems in Health Information Systems


Design choices l.jpg

CDSS Failure

Brain

HEARTFAID Decision Support and Data Processing Services

Design Choices

For some complex problem, e.g. early decompensation, no knowledge available: novel knowledge extracted by means of Artificial Neural Networks; Support Vector Machines or Bayesian Networks,…

Rule-based knowledge representation formalism more similar to experts’ reasoning mechanisms and simpler to understand for them

Algorithms for supporting clinicians’ interpretations of diagnostic exams

+

Ontologies helps standardizing terminology

Computational Reasoning

Data Processing Algorithms

Inferential Reasoning

Knowledge Discovery and Decision Support Systems in Health Information Systems


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HEARTFAID Decision Support and Data Processing Services Failure

HEARTFAID CDSS Conceptual Modeling

  • Knowledge vs. Processing levels

System components that are responsible for tasks accomplishment by using the knowledge level

All the information needed by the system for performing tasks

(e.g. data, domain knowledge, computational decision models)

Processing

Level

Knowledge

Level

Knowledge Discovery and Decision Support Systems in Health Information Systems


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HEARTFAID Decision Support and Data Processing Services Failure

HEARTFAID CDSS Architecture

Formalized Experts’ Knowledge

Computational Reasoning models

+

Data Processing Algorithms

Knowledge Discovery and Decision Support Systems in Health Information Systems


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A Significant Scenario Failure

A Significant Scenario

  • Sixty-five years old patient enrolled in HFP

    • History of acute myocardial infarction, aorto-coronary bypass

    • Currently, ischaemic dilated cardiomyopathy, with systolic disfunction

  • HEARTFAID

    • detects worsening of symptoms by telemonitoring;

    • schedules a new visit;

    • suggests clinical examinations;

    • interprets findings;

    • suggests new therapy;

    • gives hints about possible origins of symptoms worsening

Knowledge Discovery and Decision Support Systems in Health Information Systems


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A Significant Scenario Failure

Mapping onto the HEARTFAID CDSS

  • The scenario has used for defining the functioning of the CDSS

  • Each component of CDSS is triggered

  • Example of mapping for the worsening of symptoms detection by telemonitoring

Knowledge Discovery and Decision Support Systems in Health Information Systems


Telemonitoring worsening l.jpg

Patients Failure

Repository

End User

Applications

Minnesota

Questionnaire

CDSS

Agenda

//schedule a visit

A Significant Scenario

Telemonitoring: Worsening

HEARTFAID Platform

Inference on patient’s data

Ischemic Cardiomyopathy + marked activity limitation

Alert: A1, Priority: P5

Telemonitoring

CDSS Functionality:

Telemonitoring

Data Acquisition

Visit Scheduled

Request:

Interpret new data

Strategy: Trigger Inference Engine

call(InfEng(telemon, P,data_KB);

  • Alert: A1

  • Priority: P5

Knowledge Discovery and Decision Support Systems in Health Information Systems


Results l.jpg

W3C Stack of Instruments Failure

Requirements

Current Results

Results

  • Development tools were selected for fulfilling requirements of

    • Open Source

    • Upgradeability

    • Robustness

    • Ease of use

  • Semantic Web Technologieshas gathered attention within Decision Support Theory since offer

    • data integration

    • knowledge representation

    • reasoning

  • We followed the recommendations of World Wide Web Consortium (W3C)

  • Knowledge Discovery and Decision Support Systems in Health Information Systems


    Development languages and tools l.jpg

    Current Results Failure

    Development Languages and Tools

    • Web Ontology Language (OWL) for defining ontologies

    • Protégéas ontology editor

    • Semantic Web Rule Language combining OWL and Rule Mark-up Language (SWRL)

    • Jena was selected as a Java programmatic environment that includes OWL, a language for querying ontologies, SPARQL, and a rule-based inference engines

      • Other two reasoners: Bossam, Pellet

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Knowledge representation l.jpg

    Current Results Failure

    Knowledge Representation

    • A coherent and comprehensive formalization of HF domain was elicited from the guidelines of the European Society of Cardiology and a strong interaction with clinicians,

    • Starting from an existing ontology, new classes and relations were added, also in accordance to standard medical ontologies (e.g., Unified Medical Language System, UMLS)

    • An excerpt of the ontology:

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Rules formalization l.jpg

    Current Results Failure

    Rules Formalization

    • A set of rules was defined for the scenario

    • Example of natural language elicitation

      “If a patient has Left Ventricle Ejection Fraction <= 40%and he is asymptomatic and is assuming ACE Inhibitors and he had a myocardial infarction then a suggestion for the doctor is to give the patient Betablockers”

    • Translation into SWRL language

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Image processing l.jpg

    4C Failure

    2C

    ED

    ES

    Current Results

    Image Processing

    • Automated computation of Left Ventricle (LV) Ejection Fraction from Echocardiography images

    Simpson’s method:

    EDV=end diastolic volume

    ESV=end systolic volume

    EF=ejection fraction

    • Delineation of LV cavity

    • Computation of volumes and axis

    • Refinements:

      • level set method for accurate LV contour identification

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Image processing23 l.jpg

    Current Results Failure

    Image Processing

    • Extraction of LV contours: Refinement step

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Signal processing l.jpg

    Current Results Failure

    Signal Processing

    • Focus on ECG

    • QRS detection

    • QRS classification

    • Dominant beat averaging (SNR enhancement in order to provide the cardiologist with a clearer beat on which to operate the measurements)

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    The annotated database l.jpg

    Current Results Failure

    The Annotated Database

    • Real data (surface ECGs) are used from the MIT-BIH Arrhythmia Database for a total of 48 records half-hour excerpts of two-channel ambulatory ECG recordings

    • The recordings are digitized at 360 Hz with 11-bit resolution over a 10 mV range

    • Two or more cardiologists independently annotated each record; disagreements were resolved to obtain the computer-readable reference annotations for each beat (approximately 110,000 annotations in all) included in the database

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Qrs detection l.jpg

    Current Results Failure

    QRS Detection

    • Pre-filtering using a band-pass filter in the band 5-15 Hz

    • The band-pass filtered signals are used for the creation of a QRS enhanced signal (QeS)

    • The QeS is built as the sum of the absolute derivatives of each channel

    • An adaptive threshold is used for the QRS detection. The threshold is continuously updated after each QRS detection

    • To avoid indicating a T-wave (and especially large-amplitude T-waves) as another QRS, the QRS detection threshold is artificially increased after detecting a QRS peak

    • A dead-time zone of 200 msec is set up in order to reject any QRS detection closer than 200 msec to the previous one

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Results of the qrs detection l.jpg

    Current Results Failure

    Results of the QRS Detection

    • The total number of annotated beats results 109494, with 109288 TP; FN and FP are respectively 266 and 210

    • The sensitivity TP/(TP+FN) is 99.76% while the positive predictive value (PPV) TP/(TP+FP) is 99.81%

    • In 15 records a perfect detection without any FN and FP has been obtained

    • 12 records have more than 10 FP+FN and only 5 records more than 30 FP+FN

    • Sensitivity and PPV are equal or better of other algorithm published in literature and the results are obtained on all beats of the entire database while in some published studies only a subset of the total beats has been used

    • On a data set of 75 ECGs provided by Univ. Magna Graecia we had only 1 FN and 0 FP.

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Qrs classification l.jpg

    Current Results Failure

    QRS Classification

    • Feature extraction from the detected beats

    • Two-step decision tree classification

    • Preliminary results on the MIT-BIH arrhythmia database:

      • Specificity 93.70%

      • Sensitivity 98.71%

      • PPV 99.29%

      • NPV 89.02%

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    User interface for patient s management l.jpg

    IV Failure

    Current Results

    User Interface for Patient’s Management

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Conclusions future activities l.jpg

    Conclusions & Future Activities Failure

    Conclusions & Future Activities

    • Shown the current results of HEARTFAID Clinical Decision Support and Signals&Images Processing

      • Innovative conceptual modeling

      • Novel methods for aiding diagnostic examination

      • Clinicians are satisfied of preliminary results

    • Activities will be finalized in 2009 by concluding

      • The Domain Knowledge Base

      • Algorithms contained in the Model Base

      • The Signals and images analysis toolkits

      • The Meta level for integrating all the object models and the interface

      • The integration with the other platform components

    Knowledge Discovery and Decision Support Systems in Health Information Systems


    Decision support and image signal analysis in heart failure a comprehensive use case31 l.jpg

    Decision Support and Image & Signal Analysis in Heart FailureA Comprehensive Use Case

    Thanks


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