Introduction: Spontaneous intracerebral hemorrhage is the second most common type of stroke with high morbidity and mortality. Outcome prediction is very important in this disease, to enable us tailor treatment strategies especially in a low- and middle-income countries. Today, prediction is predominantly limited to few clinical factors and may not be very accurate. We explore the application of an artificial intelligence-based platform for outcome prediction with a combination of clinical, radiological, and biochemical parameters. Methods: Data from our prospectively maintained stroke register was cleaned and processed using the XGBoost machine learning (ML) algorithm to predict outcome at discharge and 90 days using the modified Rankin scale. A total of 1,000 patients were included in the study, 129 variables were pruned to 19 significant features during the phase of preprocessing. Results: The data set was split 9:1 with 900 cases being used for training and the remaining 100 for validation. The models were evaluated based on the mean absolute error (MAE). Model-1 trained for predicting “mRS_Discharge” had a MAE of 0.34 and model-2 trained for predicting “mRS_3months” had a MAE of 0.63. Conclusion: ML algorithms can be successfully applied for the prediction of outcome in intracerebral hemorrhage.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.