Litcius/Paper detail

Identifying Modifiable Predictors of Patient Outcomes After Intracerebral Hemorrhage with Machine Learning

Andrew Hall, Bradley W. Weaver, Eric M. Liotta, Matthew B. Maas, Roland Faigle, Daniel K. Mroczek, Andrew M. Naidech

2020Neurocritical Care35 citationsDOIOpen Access PDF

Topics & Concepts

MedicineGlasgow Coma ScaleIntracerebral hemorrhageModified Rankin ScaleIntraventricular hemorrhageOutcome (game theory)HematomaRandom forestDecision treeInternal medicineEmergency medicineSurgeryMachine learningComputer scienceMathematicsIschemiaPregnancyBiologyIschemic strokeMathematical economicsGeneticsGestational ageIntracerebral and Subarachnoid Hemorrhage ResearchAcute Ischemic Stroke ManagementTraumatic Brain Injury and Neurovascular Disturbances
Identifying Modifiable Predictors of Patient Outcomes After Intracerebral Hemorrhage with Machine Learning | Litcius