Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study.
Guangming ZhuBurak Berksu OzkaraHui ChenBo ZhouBin JiangVictoria Y DingMax WintermarkPublished in: The neuroradiology journal (2023)
Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.
Keyphrases
- traumatic brain injury
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- computed tomography
- contrast enhanced
- peritoneal dialysis
- prognostic factors
- artificial intelligence
- type diabetes
- severe traumatic brain injury
- metabolic syndrome
- adipose tissue
- electronic health record
- adverse drug