TY  -  
T1  - Machine learning-based dynamic mortality prediction after traumatic brain injury 
SN  -  /  
UR  - http://hdl.handle.net/10138/309013 
T3  -  
A1  - Raj, Rahul; Luostarinen, Teemu; Pursiainen, Eetu; Posti, Jussi P.; Takala, Riikka S. K.; Bendel, Stepani; Konttila, Teijo; Korja, Miikka 
A2  -  
PB  -  
Y1  - 2019 
LA  - eng 
AB  - Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion p... 
VO  -  
IS  -  
SP  -  
OP  -  
KW  - INTENSIVE-CARE-UNIT; INTRACRANIAL-PRESSURE; HOSPITAL MORTALITY; SECONDARY INSULTS; HEAD-INJURY; CLASSIFICATION; EPIDEMIOLOGY; MANAGEMENT; COMA; GUIDELINES; 3126 Surgery, anesthesiology, intensive care, radiology; 3112 Neurosciences 
N1  -   
PP  -  
ER  -