Image 2_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.jpeg

Image 2_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.jpeg

Objective<p>Neurological deterioration after mild traumatic brain injury (TBI) has been recognized as a poor prognostic factor. Early detection of neurological deterioration would allow appropriate monitoring and timely therapeutic interventions to improve patient outcomes. In this study, we d...

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Bibliographic Details
Main Author: Daisu Abe (20498225) (author)
Other Authors: Motoki Inaji (6734660) (author), Takeshi Hase (257672) (author), Eiichi Suehiro (6104819) (author), Naoto Shiomi (20498228) (author), Hiroshi Yatsushige (20498231) (author), Shin Hirota (19746329) (author), Shu Hasegawa (20498234) (author), Hiroshi Karibe (20498237) (author), Akihiro Miyata (20498240) (author), Kenya Kawakita (4709071) (author), Kohei Haji (20498243) (author), Hideo Aihara (20498246) (author), Shoji Yokobori (8600112) (author), Takeshi Maeda (304955) (author), Takahiro Onuki (20498249) (author), Kotaro Oshio (20498252) (author), Nobukazu Komoribayashi (20498255) (author), Michiyasu Suzuki (436074) (author), Taketoshi Maehara (3573644) (author)
Published: 2025
Subjects:
Neurology and Neuromuscular Diseases
mild traumatic brain injury
neurological deterioration
machine learning
predictive model
XGBoost
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