Improving diagnosis of acute coronary syndromes in an emergency setting: A machine learning approach
(2008)- 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.
- Abstract (Swedish)
- Popular Abstract in Swedish
Akut koronart syndrom (AKS) är den största folkdödaren i väst idag. Trots välutbildade läkare och bra diagnostiska verktyg så är det forfarande svårt att ställa en diagnos tidigt på sjukhusens akutavdelningar. I den här avhandlingen undersöker vi möjligheter att i ett tidigt skede förutsäga AKS med hjälp av maskininlärning. Främst användes logistiska regressionsmodeller och kommitteer av artificiella neurala nätverk (ANN). Jämförelser med expertläkare genomfördes kontinuerligt som en kvalitetskontroll. Vi utvecklade även praktiska patientbaserade förklaringsmodeller för ett ANNs beslutsprocess.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1151299
- author
- Green, Michael LU
- supervisor
- opponent
-
- Professor Lisboa, Paulo, Liverpool John Moores University, Liverpool, England
- organization
- publishing date
- 2008
- type
- Thesis
- publication status
- published
- subject
- keywords
- ensemble, artificial neural network, machine learning, acute coronary syndrome, electrocardiogram, case based explanation, decision support system
- pages
- 147 pages
- defense location
- Lecture hall F, Department of Theoretical Physics, Sölvegatan 14A, SE-223 62 Lund, Sweden
- defense date
- 2008-06-18 10:15:00
- ISBN
- 978-91-628-7434-6
- language
- English
- LU publication?
- yes
- id
- 4415dd7b-68c7-4464-a2a2-1a7797300988 (old id 1151299)
- date added to LUP
- 2016-04-04 14:25:19
- date last changed
- 2018-11-21 21:20:14
@phdthesis{4415dd7b-68c7-4464-a2a2-1a7797300988, 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.}}, author = {{Green, Michael}}, isbn = {{978-91-628-7434-6}}, keywords = {{ensemble; artificial neural network; machine learning; acute coronary syndrome; electrocardiogram; case based explanation; decision support system}}, language = {{eng}}, school = {{Lund University}}, title = {{Improving diagnosis of acute coronary syndromes in an emergency setting: A machine learning approach}}, url = {{https://lup.lub.lu.se/search/files/6356632/1151303.pdf}}, year = {{2008}}, }