Functional Outcome Prediction After Spinal Cord Injury Using Ensemble Machine Learning.

Abstract

To establish a machine learning model to predict functional outcomes after SCI with Spinal Cord Independence Measure (SCIM) using features present at the time of rehabilitation admission. A retrospective, single-center study. The following data were collected from the medical charts: age, sex, acute length of stay (LOS), level of injury, American Spinal Injury Association Impairment Scale (AIS), motor scores of each key muscle, Upper Extremity Motor Score (UEMS), Lower Extremity Motor Score (LEMS), SCIM total scores, and subtotal scores on admission and discharge. Based on the multivariate linear regression analysis, age, acute LOS, UEMS, LEMS, and SCIM subtotal scores were selected as features for machine learning algorithms. Random forest, support vector machine, neural network, and gradient boosting were used as the base models and combined using ridge regression as a metamodel. A spinal center in Tokyo, Japan. Participants were individuals with SCI admitted to our hospital from March 2016 to October 2021 for the first rehabilitation after the injury. They were divided into 2 groups: training (n=140) and testing (n=70). Not applicable. The root-mean-square error (RMSE), R², and Mean Absolute Error (MAE) were used as accuracy measures. RMSE, R², and MAE of the meta-model using the testing group were 9.7453, 0.8835, and 7.4743, respectively, outperforming any other single base model. Our study revealed that functional prognostication could be achieved using machine-learning methods with features present at the time of rehabilitation admission. Goals can be set at the beginning of rehabilitation. Moreover, our model can be used to evaluate advanced medical treatments, such as regenerative medicine.