Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients.
David ZarrinAbhinav SuriKaren McCarthyBilwaj GaonkarBayard WilsonGeoffrey ColbyRobert FreundlichLuke MacyszynEilon GabelPublished in: Research square (2024)
We present an accurate (AUC=0.88) and early (>1 week prior) predictor of CVRV using machine learning over two large cohorts of subarachnoid hemorrhage patients at separate institutions. This represents a significant step towards optimized clinical management and improved resource allocation in the intensive care setting of subarachnoid hemorrhage patients.
Keyphrases
- subarachnoid hemorrhage
- brain injury
- cerebral ischemia
- end stage renal disease
- machine learning
- ejection fraction
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- randomized controlled trial
- prognostic factors
- clinical trial
- patient reported outcomes
- high resolution
- artificial intelligence
- patient reported
- big data