Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: a data-based study - Dang, Hui; Su, Wenlong; Tang, Zhiqing; Yue, Shouwei; Zhang, Hao (Frontiers in Neuroscience, 2023)
Objective:Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. In this study, the characteristics of the patients, who were admitted to the China Rehabilitation Research Center, were elucidated in the TBI database, and a prediction model based on the Fugl-Meyer assessment (FMA) was established using this database. Method: A retrospective analysis of 463 TBI patients, who were hospitalized from June 2016 to June 2020, was performed. The data of the patients used for this study included the age and gender of the patients, course of TBI, complications, and concurrent dysfunctions, which were assessed using FMA and other measures. The information was collected at the time of admission to the hospital and one month after hospitalization. After one month, a prediction model, based on the correlation analyses and a 3-layer back propagation neural network, was established to predict the FMA. The correlations and error distribution between the predicted and actual values were described. Results: Most of the TBI patients, included in this study, had severe conditions (70%). The main causes of the TBI were car accidents (56.59%), while the most common complication and dysfunctions were hydrocephalus (46.44%) and cognitive and motor dysfunction (65.23% and 63.50%), respectively. A total of 233 patients were used in the prediction model, studying the 11 prognostic factors, such as gender, course of the disease, epilepsy, and hydrocephalus. The correlation between the predicted and the actual value was R2 = 0.98 with satisfying error distribution [-15.16-16.05 (3.70 ± 3.47)].