Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This r...

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Veröffentlicht in:Diagnostics (Basel) 2021-10, Vol.11 (10), p.1909, Article 1909
Hauptverfasser: Park, Dougho, Jeong, Eunhwan, Kim, Haejong, Pyun, Hae Wook, Kim, Haemin, Choi, Yeon-Ju, Kim, Youngsoo, Jin, Suntak, Hong, Daeyoung, Lee, Dong Woo, Lee, Su Yun, Kim, Mun-Chul
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container_issue 10
container_start_page 1909
container_title Diagnostics (Basel)
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creator Park, Dougho
Jeong, Eunhwan
Kim, Haejong
Pyun, Hae Wook
Kim, Haemin
Choi, Yeon-Ju
Kim, Youngsoo
Jin, Suntak
Hong, Daeyoung
Lee, Dong Woo
Lee, Su Yun
Kim, Mun-Chul
description Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.
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We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC &gt; 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.</description><identifier>ISSN: 2075-4418</identifier><identifier>EISSN: 2075-4418</identifier><identifier>DOI: 10.3390/diagnostics11101909</identifier><identifier>PMID: 34679606</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject>algorithm ; Algorithms ; Cardiac arrhythmia ; Cardiovascular disease ; Datasets ; Disability ; functional outcome ; General &amp; Internal Medicine ; Hospitals ; ischemic stroke ; Life Sciences &amp; Biomedicine ; Machine learning ; Medicine, General &amp; Internal ; Patients ; prediction ; Science &amp; Technology ; Stroke ; Variables ; Vein &amp; artery diseases</subject><ispartof>Diagnostics (Basel), 2021-10, Vol.11 (10), p.1909, Article 1909</ispartof><rights>2021 by the authors. 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/). 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subjects algorithm
Algorithms
Cardiac arrhythmia
Cardiovascular disease
Datasets
Disability
functional outcome
General & Internal Medicine
Hospitals
ischemic stroke
Life Sciences & Biomedicine
Machine learning
Medicine, General & Internal
Patients
prediction
Science & Technology
Stroke
Variables
Vein & artery diseases
title Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea
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