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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Chen-Chih Chung, Lung Chan, Oluwaseun Adebayo Bamodu, Chien-Tai Hong, Hung-Wen Chiu
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
R
Q
Acceso en línea:https://doaj.org/article/fc9048def42f4e838df74d47b491ac33
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:fc9048def42f4e838df74d47b491ac33
record_format dspace
spelling 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)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle 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
description 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 AT chenchihchung artificialneuralnetworkbasedpredictionofpostthrombolysisintracerebralhemorrhageanddeath
AT lungchan artificialneuralnetworkbasedpredictionofpostthrombolysisintracerebralhemorrhageanddeath
AT oluwaseunadebayobamodu artificialneuralnetworkbasedpredictionofpostthrombolysisintracerebralhemorrhageanddeath
AT chientaihong artificialneuralnetworkbasedpredictionofpostthrombolysisintracerebralhemorrhageanddeath
AT hungwenchiu artificialneuralnetworkbasedpredictionofpostthrombolysisintracerebralhemorrhageanddeath
_version_ 1718387792295231488