title: Hybrid outcome prediction model for severe traumatic brain injury.
abstract: Numerous studies addressing different methods of head injury prognostication have been published. Unfortunately, these studies often incorporate different head injury prognostication models and study populations, thus making direct comparison difficult, if not impossible. Furthermore, newer artificial intelligence tools such as machine learning methods have evolved in the field of data analysis, alongside more traditional methods of analysis. This study targets the development of a set of integrated prognostication model combining different classes of outcome and prognostic factors. Methodologies such as discriminant analysis, logistic regression, decision tree, Bayesian network, and neural network were employed in the study. Several prognostication models were developed using prospectively collected data from 513 severe closed head-injured patients admitted to the Neurocritical Unit at National Neuroscience Institute of Singapore, from April 1999 to February 2003. The correlation between prognostic factors at admission and outcome at 6 months following injury was studied. Overfitting error, which may falsely distinguish different outcomes, was compared graphically. Tenfold cross-validation technique, which reduces overfitting error, was used to validate outcome prediction accuracy. The overall prediction accuracy achieved ranged from 49.79% to 81.49%. Consistently high outcome prediction accuracy was seen with logistic regression and decision tree. Combining both logistic regression and decision tree models, a hybrid prediction model was then developed. This hybrid model would more accurately predict the 6-month post-severe head injury outcome using baseline admission parameters.
authors.affiliation: Acute Brain Injury Research Laboratory, Department of Neurosurgery, National Neuroscience Institute, 11 Jalan Tan Tock Seng, 308433 Singapore.
authors.forename: Boon Chuan
authors.initials: BC
authors.lastname: Pang
authors.forename: Vellaisamy
authors.initials: V
authors.lastname: Kuralmani
authors.forename: Rohit
authors.initials: R
authors.lastname: Joshi
authors.forename: Yin
authors.initials: Y
authors.lastname: Hongli
authors.forename: Kah Keow
authors.initials: KK
authors.lastname: Lee
authors.forename: Beng Ti
authors.initials: BT
authors.lastname: Ang
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
authors.forename: Tze Yun
authors.initials: TY
authors.lastname: Leong
authors.forename: Ivan
authors.initials: I
authors.lastname: Ng
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001794:Blood Pressure / Q000502:physiology; D020201:Brain Hemorrhage, Traumatic / Q000453:epidemiology / Q000473:pathology* / Q000601:surgery; D002560:Cerebrovascular Circulation / Q000502:physiology; D003663:Decision Trees; D005260:Female; D023261:Glasgow Outcome Scale; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D016896:Treatment Outcome