首页|Albert Einstein College of Medicine Reports Findings in Subarachnoid Hemorrhage (Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study)
Albert Einstein College of Medicine Reports Findings in Subarachnoid Hemorrhage (Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study)
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New research on Central Nervous System Diseases and Conditions - Subarachnoid Hemorrhage is the subject of a report. According to news reporting originating in Bronx, New York, by NewsRx journalists, research stated, “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.” The news reporters obtained a quote from the research from the Albert Einstein College of Medicine, “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. 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. 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.”
BronxNew YorkUnited StatesNorth and Central AmericaBrain Diseases and ConditionsBrain IschemiaCardiovascular Diseases and ConditionsCentral Nervous System Diseases and ConditionsCerebrovascular DisordersCyborgsEmerging TechnologiesHealth and MedicineIntracranial HemorrhagesIschemiaMachine LearningPerceptronSubarachnoid HemorrhageVascular Diseases and Conditions
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