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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 ; machinelearning ; medical decision making ; neural networks ; stroke
Background and Purpose- Thepredictionof long-termoutcomesin ischemicstrokepatients may be useful in treatment decisions.Machinelearning techniques are being increasingly adapted for use in the medical field because of their high accuracy. This study investigated the applicability ofmachinelearning techniques topredictlong-termoutcomesin ischemicstrokepatients. Methods- This was a retrospective study using a prospective cohort that enrolled patients withacuteischemicstroke. Favorableoutcomewas defined as modified Rankin Scale score 0, 1, or 2 at 3 months. We developed 3machinelearningmodels(deep neural network, random forest, and logistic regression) and compared theirpredictability. To evaluate the accuracy of themachinelearningmodels, we also compared them to theAcute StrokeRegistry and Analysis of Lausanne (ASTRAL) score. Results- A total of 2604 patients were included in this study, and 2043 (78%) of them had favorableoutcomes. The area under the curve for the deep neural networkmodelwas 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)modelswere not significantly higher than that of the ASTRAL score. Using only the 6 variables that are used for the ASTRAL score, the performance of themachinelearningmodelsdid not significantly differ from that of the ASTRAL score. Conclusions-Machinelearning algorithms, particularly the deep neural network, can improve thepredictionof long-termoutcomesin ischemicstrokepatients.
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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 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) ORCID logo https://orcid.org/0000-0002-4401-7803
Heo, JoonNyung(허준녕)
Heo, Ji Hoe(허지회) ORCID logo https://orcid.org/0000-0001-9898-3321
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