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. Attached Files Name Description MIMEType Size Downloads asadi-machinelearning-2014.pdf Published version application/pdf 1.32MB 91 Title Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy Author(s) 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30092712 Document type: Journal Article Collections: Faculty of Health School of Medicine Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Mon, 03 Apr 2017, 12:25:23 EST Tue, 04 Apr 2017, 05:04:03 EST Tue, 04 Apr 2017, 08:13:58 EST Sat, 08 Apr 2017, 02:30:33 EST Tue, 11 Apr 2017, 06:07:15 EST Mon, 10 Jul 2017, 08:39:08 EST Mon, 10 Jul 2017, 08:41:32 EST Mon, 10 Jul 2017, 08:43:08 EST Tue, 11 Jul 2017, 12:53:24 EST Fri, 07 Sep 2018, 12:39:32 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 81 times in Scopus Search Google Scholar Access Statistics: 120 Abstract Views, 91 File Downloads  -  Detailed Statistics Created: Mon, 03 Apr 2017, 12:25:23 EST

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.