Study and Validation Protocol of Risk Prediction Model for Deep Venous Thrombosis After Severe Traumatic Brain Injury Based on Machine Learning Algorithms

Traumatic Brain Injury (TBI) is a severe traumatic disease, mostly caused by external forces such as traffic accidents, collisions, falls from heights, compression, and head trauma, leading to organic damage to brain tissue. Postoperatively, patients are at risk of Deep Vein Thrombosis (DVT), directly impacting their postoperative quality of life. This paper designs observational clinical studies and validation schemes related to interdisciplinary research on this matter, aiming to study the risk prediction model of DVT in patients with severe TBI postoperatively using machine learning algorithms. It aims to grasp the risk factors for DVT in patients with severe TBI postoperatively and thoroughly analyze the relationship between these risk factors. Using a risk prediction model for DVT in patients with severe TBI postoperatively as guidance, this research constructs a model based on risk prediction line charts and conducts empirical studies to validate the application effect of the model, enabling early identification and accurate prediction of the occurrence of DVT in patients with severe TBI postoperatively. Guided by the risk prediction model for DVT in patients with severe TBI postoperatively, it provides medical personnel with effective reference for identifying and preventing DVT in patients with severe TBI postoperatively, and provides nursing reference basis for individualized prediction of DVT occurrence risk in clinical practice, thereby significantly improving the nursing experience of patients in the hospital.