Improving diagnosis of acute coronary syndromes in an emergency setting: A machine learning approach

Michael Green

    Research output: ThesisDoctoral Thesis (compilation)

    131 Downloads (Pure)

    Abstract

    Acute coronary syndrome (ACS) is the biggest people killer in the western world today. Despite well trained physicians and reliable diagnostic tools, diagnosing ACS early in the emergency departments (ED) remains a challenge. In this thesis we used machine learning, via logistic regression models and artificial neural network ensembles, to investigate the possibility of predicting ACS at an early stage using electrocardiogram data. Thorough comparisons were made to several expert physicians, currently working in the ED, to verify the models. In the context of neural networks we developed methods for the case based explanation of their decisions.
    Original languageEnglish
    QualificationDoctor
    Awarding Institution
    Supervisors/Advisors
    • Ohlsson, Mattias, Supervisor
    • Edenbrandt, Lars, Supervisor
    Award date2008 Jun 18
    ISBN (Print)978-91-628-7434-6
    Publication statusPublished - 2008

    Bibliographical note

    Defence details

    Date: 2008-06-18
    Time: 10:15
    Place: Lecture hall F, Department of Theoretical Physics, Sölvegatan 14A, SE-223 62 Lund, Sweden

    External reviewer(s)

    Name: Lisboa, Paulo
    Title: Professor
    Affiliation: Liverpool John Moores University, Liverpool, England

    ---

    Subject classification (UKÄ)

    • Biophysics

    Free keywords

    • ensemble
    • artificial neural network
    • machine learning
    • acute coronary syndrome
    • electrocardiogram
    • case based explanation
    • decision support system

    Fingerprint

    Dive into the research topics of 'Improving diagnosis of acute coronary syndromes in an emergency setting: A machine learning approach'. Together they form a unique fingerprint.

    Cite this