Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
Abstract 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...
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2020
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oai:doaj.org-article:fc9048def42f4e838df74d47b491ac332021-12-02T15:10:06ZArtificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death10.1038/s41598-020-77546-52045-2322https://doaj.org/article/fc9048def42f4e838df74d47b491ac332020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77546-5https://doaj.org/toc/2045-2322Abstract 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.Chen-Chih ChungLung ChanOluwaseun Adebayo BamoduChien-Tai HongHung-Wen ChiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020) |
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Medicine R Science Q Chen-Chih Chung Lung Chan Oluwaseun Adebayo Bamodu Chien-Tai Hong Hung-Wen Chiu Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death |
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Abstract 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. |
format |
article |
author |
Chen-Chih Chung Lung Chan Oluwaseun Adebayo Bamodu Chien-Tai Hong Hung-Wen Chiu |
author_facet |
Chen-Chih Chung Lung Chan Oluwaseun Adebayo Bamodu Chien-Tai Hong Hung-Wen Chiu |
author_sort |
Chen-Chih Chung |
title |
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_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_sort |
artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death |
publisher |
Nature Portfolio |
publishDate |
2020 |
url |
https://doaj.org/article/fc9048def42f4e838df74d47b491ac33 |
work_keys_str_mv |
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1718387792295231488 |