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Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models
At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term fun...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918022/ https://www.ncbi.nlm.nih.gov/pubmed/36769491 http://dx.doi.org/10.3390/jcm12030839 |
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author | Ozkara, Burak B. Karabacak, Mert Hamam, Omar Wang, Richard Kotha, Apoorva Khalili, Neda Hoseinyazdi, Meisam Chen, Melissa M. Wintermark, Max Yedavalli, Vivek S. |
author_facet | Ozkara, Burak B. Karabacak, Mert Hamam, Omar Wang, Richard Kotha, Apoorva Khalili, Neda Hoseinyazdi, Meisam Chen, Melissa M. Wintermark, Max Yedavalli, Vivek S. |
author_sort | Ozkara, Burak B. |
collection | PubMed |
description | At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions. |
format | Online Article Text |
id | pubmed-9918022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99180222023-02-11 Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models Ozkara, Burak B. Karabacak, Mert Hamam, Omar Wang, Richard Kotha, Apoorva Khalili, Neda Hoseinyazdi, Meisam Chen, Melissa M. Wintermark, Max Yedavalli, Vivek S. J Clin Med Article At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions. MDPI 2023-01-20 /pmc/articles/PMC9918022/ /pubmed/36769491 http://dx.doi.org/10.3390/jcm12030839 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ozkara, Burak B. Karabacak, Mert Hamam, Omar Wang, Richard Kotha, Apoorva Khalili, Neda Hoseinyazdi, Meisam Chen, Melissa M. Wintermark, Max Yedavalli, Vivek S. Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models |
title | Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models |
title_full | Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models |
title_fullStr | Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models |
title_full_unstemmed | Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models |
title_short | Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models |
title_sort | prediction of functional outcome in stroke patients with proximal middle cerebral artery occlusions using machine learning models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9918022/ https://www.ncbi.nlm.nih.gov/pubmed/36769491 http://dx.doi.org/10.3390/jcm12030839 |
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