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Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death

Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-ba...

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Autores principales: Chung, Chen-Chih, Chan, Lung, Bamodu, Oluwaseun Adebayo, Hong, Chien-Tai, Chiu, Hung-Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689530/
https://www.ncbi.nlm.nih.gov/pubmed/33239681
http://dx.doi.org/10.1038/s41598-020-77546-5
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author Chung, Chen-Chih
Chan, Lung
Bamodu, Oluwaseun Adebayo
Hong, Chien-Tai
Chiu, Hung-Wen
author_facet Chung, Chen-Chih
Chan, Lung
Bamodu, Oluwaseun Adebayo
Hong, Chien-Tai
Chiu, Hung-Wen
author_sort Chung, Chen-Chih
collection PubMed
description Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged.
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spelling pubmed-76895302020-11-27 Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death Chung, Chen-Chih Chan, Lung Bamodu, Oluwaseun Adebayo Hong, Chien-Tai Chiu, Hung-Wen Sci Rep Article Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intracerebral hemorrhage (sICH) remains a frequent complication and constitutes a major concern when treating acute ischemic stroke (AIS). This study explored the use of artificial neural network (ANN)-based models to predict sICH and 3-month mortality for patients with AIS receiving tPA. We developed ANN models based on evaluation of the predictive value of pre-treatment parameters associated with sICH and mortality in a cohort of 331 patients between 2009 and 2018. The ANN models were generated using eight clinical inputs and two outputs. The generalizability of the model was validated using fivefold cross-validation. The performance of each model was assessed according to the accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). After adequate training, the ANN predictive model AUC for sICH was 0.941, with accuracy, sensitivity, and specificity of 91.0%, 85.7%, and 92.5%, respectively. The predictive model AUC for 3-month mortality was 0.976, with accuracy, sensitivity, and specificity of 95.2%, 94.4%, and 95.5%, respectively. The generated ANN-based models exhibited high predictive performance and reliability for predicting sICH and 3-month mortality after thrombolysis; thus, its clinical application to assist decision-making when administering tPA is envisaged. Nature Publishing Group UK 2020-11-25 /pmc/articles/PMC7689530/ /pubmed/33239681 http://dx.doi.org/10.1038/s41598-020-77546-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Chung, Chen-Chih
Chan, Lung
Bamodu, Oluwaseun Adebayo
Hong, Chien-Tai
Chiu, Hung-Wen
Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
title Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
title_full Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
title_fullStr Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
title_full_unstemmed Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
title_short Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
title_sort artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689530/
https://www.ncbi.nlm.nih.gov/pubmed/33239681
http://dx.doi.org/10.1038/s41598-020-77546-5
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