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Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy

Asadi, Hamed, Dowling, Richard, Yan, Bernard and Mitchell, Peter 2014, Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy, PLoS one, vol. 9, no. 2, pp. 1-11, doi: 10.1371/journal.pone.0088225.

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Title Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy
Author(s) Asadi, HamedORCID iD for Asadi, Hamed orcid.org/0000-0003-2475-9727
Dowling, Richard
Yan, Bernard
Mitchell, Peter
Journal name PLoS one
Volume number 9
Issue number 2
Article ID e88225
Start page 1
End page 11
Total pages 11
Publisher Public Library of Science
Place of publication San Francisco, Calif.
Publication date 2014-02
ISSN 1932-6203
Keyword(s) Adult
Aged
Artificial Intelligence
Brain Ischemia
Endovascular Procedures
Female
Humans
Intracranial Hemorrhages
Male
Models, Theoretical
Neural Networks (Computer)
ROC Curve
Stroke
Support Vector Machine
Treatment Outcome
Summary Introduction
Stroke is a major cause of death and disability. Accurately predicting stroke outcome from a set of predictive variables may identify high-risk patients and guide treatment approaches, leading to decreased morbidity. Logistic regression models allow for the identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to design and compare different machine learning methods, capable of predicting the outcome of endovascular intervention in acute anterior circulation ischaemic stroke.

Method
We conducted a retrospective study of a prospectively collected database of acute ischaemic stroke treated by endovascular intervention. Using SPSS®, MATLAB®, and Rapidminer®, classical statistics as well as artificial neural network and support vector algorithms were applied to design a supervised machine capable of classifying these predictors into potential good and poor outcomes. These algorithms were trained, validated and tested using randomly divided data.

Results

We included 107 consecutive acute anterior circulation ischaemic stroke patients treated by endovascular technique. Sixty-six were male and the mean age of 65.3. All the available demographic, procedural and clinical factors were included into the models. The final confusion matrix of the neural network, demonstrated an overall congruency of ∼80% between the target and output classes, with favourable receiving operative characteristics. However, after optimisation, the support vector machine had a relatively better performance, with a root mean squared error of 2.064 (SD: ±0.408).

Discussion
We showed promising accuracy of outcome prediction, using supervised machine learning algorithms, with potential for incorporation of larger multicenter datasets, likely further improving prediction. Finally, we propose that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke.
Language eng
DOI 10.1371/journal.pone.0088225
Field of Research 110399 Clinical Sciences not elsewhere classified
MD Multidisciplinary
Socio Economic Objective 929999 Health not elsewhere classified
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2014, Asadi et al
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092712

Document type: Journal Article
Collections: Faculty of Health
School of Medicine
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.