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Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke

 JoonNyung Heo  ;  Jihoon G. Yoon  ;  Hyungjong Park  ;  Young Dae Kim  ;  Hyo Suk Nam  ;  Ji Hoe Heo 
 STROKE, Vol.50(5) : 1263-1265, 2019 
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cerebral infarction ;  machine learning ; medical decision making ; neural networks ;  stroke
Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techniques are being increasingly adapted for use in the medical field because of their high accuracy. This study investigated the applicability of machine learning techniques to predict long-term outcomes in ischemic strokepatients. Methods- This was a retrospective study using a prospective cohort that enrolled patients with acute ischemic stroke. Favorable outcome was defined as modified Rankin Scale score 0, 1, or 2 at 3 months. We developed 3 machine learning models (deep neural network, random forest, and logistic regression) and compared their predictability. To evaluate the accuracy of the machine learning models, we also compared them to the Acute StrokeRegistry and Analysis of Lausanne (ASTRAL) score. Results- A total of 2604 patients were included in this study, and 2043 (78%) of them had favorable outcomes. The area under the curve for the deep neural network model was significantly higher than that of the ASTRAL score (0.888 versus 0.839; P<0.001), while the areas under the curves of the random forest (0.857; P=0.136) and logistic regression (0.849; P=0.413) models were not significantly higher than that of the ASTRAL score. Using only the 6 variables that are used for the ASTRAL score, the performance of the machine learning models did not significantly differ from that of the ASTRAL score. Conclusions- Machine learning algorithms, particularly the deep neural network, can improve the prediction of long-term outcomes in ischemic stroke patients.
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1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Young Dae(김영대) ORCID logo https://orcid.org/0000-0001-5750-2616
Nam, Hyo Suk(남효석) ORCID logo https://orcid.org/0000-0002-4415-3995
Park, Hyungjong(박형종)
Yoon, Jihoon G.(윤지훈)
Heo, Joon Nyung(허준녕)
Heo, Ji Hoe(허지회) ORCID logo https://orcid.org/0000-0001-9898-3321
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