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Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke
Stroke (1970), 2019-05, Vol.50 (5), p.1263-1265
[Peer Reviewed Journal]
2019 American Heart Association, Inc. ;ISSN: 0039-2499 ;EISSN: 1524-4628 ;DOI: 10.1161/STROKEAHA.118.024293 ;PMID: 30890116
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Title:
Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke
Author:
Heo, JoonNyung
;
Yoon, Jihoon G
;
Park, Hyungjong
;
Kim, Young Dae
;
Nam, Hyo Suk
;
Heo, Ji Hoe
Subjects:
Cohort Studies
;
Humans
;
Machine Learning - trends
;
Neural Networks, Computer
;
Predictive Value of Tests
;
Prospective Studies
;
Retrospective Studies
;
Stroke - diagnosis
;
Stroke - therapy
;
Treatment Outcome
Is Part Of:
Stroke (1970), 2019-05, Vol.50 (5), p.1263-1265
Description:
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 stroke patients. 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 Stroke Registry 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.
Publisher:
United States: American Heart Association, Inc
Language:
English
Identifier:
ISSN: 0039-2499
EISSN: 1524-4628
DOI: 10.1161/STROKEAHA.118.024293
PMID: 30890116
Source:
MEDLINE
Alma/SFX Local Collection
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