JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

Détails

ID Serval
serval:BIB_B430AE82359F
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.
Périodique
AJR. American journal of roentgenology
Auteur(s)
Xie Y., Jiang B., Gong E., Li Y., Zhu G., Michel P., Wintermark M., Zaharchuk G.
ISSN
1546-3141 (Electronic)
ISSN-L
0361-803X
Statut éditorial
Publié
Date de publication
01/2019
Peer-reviewed
Oui
Volume
212
Numéro
1
Pages
44-51
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision-making process involves numerous biomarkers including imaging features and demographic information. The objective of this study was to integrate common stroke biomarkers using machine learning methods and predict patient recovery outcome at 90 days.
A total of 512 patients were enrolled in this retrospective study. Extreme gradient boosting (XGB) and gradient boosting machine (GBM) models were used to predict modified Rankin scale (mRS) scores at 90 days using biomarkers available at admission and 24 hours. Feature selections were performed using a greedy algorithm. Fivefold cross validation was applied to estimate model performance.
For binary prediction of an mRS score of greater than 2 using biomarkers available at admission, XGB and GBM had an AUC of 0.746 and 0.748, respectively. Adding the National Institutes of Health Stroke Score at 24 hours and performing feature selection improved the AUC of XGB to 0.884 and the AUC of GBM to 0.877. With the addition of the recanalization outcome, XGB's AUC improved to 0.807 for nonrecanalized patients and dropped to 0.670 for recanalized patients. GBM's AUC improved to 0.781 for nonrecanalized patients and dropped to 0.655 for recanalized patients.
Decision tree-based GBMs can predict the recovery outcome of stroke patients at admission with a high AUC. Breaking down the patient groups on the basis of recanalization and nonrecanalization can potentially help with the treatment decision process.
Mots-clé
CT, machine learning, modified Rankin scale, prediction, stroke
Pubmed
Web of science
Création de la notice
05/11/2018 9:54
Dernière modification de la notice
20/08/2019 16:22
Données d'usage