TY - JOUR
T1 - Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study
AU - Azzam, Ahmed Y.
AU - Vaishnav, Dhrumil
AU - Essibayi, Muhammed Amir
AU - Unda, Santiago R.
AU - Jabal, Mohamed Sobhi
AU - Liriano, Genesis
AU - Fortunel, Adisson
AU - Holland, Ryan
AU - Khatri, Deepak
AU - Haranhalli, Neil
AU - Altschul, David
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2024/4
Y1 - 2024/4
N2 - Introduction: Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification. Methods: In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH. Patient data, including various clinical variables and co-factors, were collected. Six different machine learning models, including logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting (XGB), were trained and evaluated using performance metrics such as accuracy, area under the curve (AUC), precision, recall, and F1 score. Results: After data augmentation, the random forest model demonstrated the best performance, with an AUC of 0.85. The multilayer perceptron neural network model achieved an accuracy of 0.93 and an F1 score of 0.85, making it the best performing model. The presence of positive clinical vasospasm was identified as the most important feature for predicting DCI. Conclusions: Our study highlights the potential of machine learning models in predicting the occurrence of DCI in patients with aSAH. The multilayer perceptron model showed excellent performance, indicating its utility in risk stratification and clinical decision-making. However, further validation and refinement of the models are necessary to ensure their generalizability and applicability in real-world settings. Machine learning techniques have the potential to enhance patient care and improve outcomes in aSAH, but their implementation should be accompanied by careful evaluation and validation.
AB - Introduction: Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification. Methods: In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH. Patient data, including various clinical variables and co-factors, were collected. Six different machine learning models, including logistic regression, multilayer perceptron, decision tree, random forest, gradient boosting machine, and extreme gradient boosting (XGB), were trained and evaluated using performance metrics such as accuracy, area under the curve (AUC), precision, recall, and F1 score. Results: After data augmentation, the random forest model demonstrated the best performance, with an AUC of 0.85. The multilayer perceptron neural network model achieved an accuracy of 0.93 and an F1 score of 0.85, making it the best performing model. The presence of positive clinical vasospasm was identified as the most important feature for predicting DCI. Conclusions: Our study highlights the potential of machine learning models in predicting the occurrence of DCI in patients with aSAH. The multilayer perceptron model showed excellent performance, indicating its utility in risk stratification and clinical decision-making. However, further validation and refinement of the models are necessary to ensure their generalizability and applicability in real-world settings. Machine learning techniques have the potential to enhance patient care and improve outcomes in aSAH, but their implementation should be accompanied by careful evaluation and validation.
KW - Aneurysm
KW - Delayed cerebral ischemia
KW - Machine learning
KW - Subarachnoid hemorrhage
KW - Vasospasm
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U2 - 10.1016/j.jstrokecerebrovasdis.2023.107553
DO - 10.1016/j.jstrokecerebrovasdis.2023.107553
M3 - Article
AN - SCOPUS:85184782320
SN - 1052-3057
VL - 33
JO - Journal of Stroke and Cerebrovascular Diseases
JF - Journal of Stroke and Cerebrovascular Diseases
IS - 4
M1 - 107553
ER -