Rasoolinejad Mohammad, Say Irene, Wu Peter B, Liu Xinran, Zhou Yan, Zhang Nathan, Rosario Emily R, Lu Daniel C
Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, United States.
Front Rehabil Sci. 2025 Aug 25;6:1594753. doi: 10.3389/fresc.2025.1594753. eCollection 2025.
Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.
We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes. The primary outcome was the Functional Independence Measure (FIM) score at discharge, reflecting the level of independence achieved by patients after comprehensive inpatient rehabilitation.
Tree-based algorithms, particularly Random Forest (RF) and XGBoost, significantly outperformed traditional statistical models and Generalized Linear Models (GLMs) in predicting discharge FIM scores. The RF model exhibited the highest predictive accuracy, with an R-squared value of 0.90 and a Mean Squared Error (MSE) of 0.29 on the training dataset, while achieving 0.52 R-squared and 1.37 MSE on the test dataset. The XGBoost model also demonstrated strong performance, with an R-squared value of 0.74 and an MSE of 0.75 on the training dataset, and 0.51 R-squared with 1.39 MSE on the test dataset. Our analysis identified key predictors of rehabilitation outcomes, including the initial FIM scores and specific demographic factors such as level of injury and prehospital living settings. The study also highlighted the superior ability of tree-based models to capture the complex, non-linear relationships between variables that impact recovery in SCI patients.
This research underscores the potential of machine learning models to enhance the accuracy of outcome predictions in SCI rehabilitation. The findings support the integration of these advanced predictive tools in clinical settings to better guide decision making for patients and families, tailor rehabilitation plans, allocate resources efficiently, and ultimately improve patient outcomes.
脊髓损伤（SCI）给患者、家庭和医疗系统带来了沉重负担。准确预测脊髓损伤患者的功能结局对于优化康复策略、指导患者及其家属的决策以及改善患者护理至关重要。
我们对一家急性康复机构收治的589例脊髓损伤患者进行了回顾性分析，并使用该数据集训练先进的机器学习算法来预测患者的康复结局。主要结局是出院时的功能独立性测量（FIM）评分，反映患者在综合住院康复后实现的独立水平。
基于树的算法，特别是随机森林（RF）和极端梯度提升（XGBoost），在预测出院FIM评分方面显著优于传统统计模型和广义线性模型（GLM）。RF模型表现出最高的预测准确性，在训练数据集上的决定系数（R平方）值为0.90，均方误差（MSE）为0.29，而在测试数据集上的R平方为0.52，MSE为1.37。XGBoost模型也表现出强大的性能，在训练数据集上的R平方值为0.74，MSE为0.75，在测试数据集上的R平方为0.51，MSE为1.39。我们的分析确定了康复结局的关键预测因素，包括初始FIM评分以及特定的人口统计学因素，如损伤水平和院前生活环境。该研究还强调了基于树的模型在捕捉影响脊髓损伤患者恢复的变量之间复杂的非线性关系方面的卓越能力。
本研究强调了机器学习模型在提高脊髓损伤康复结局预测准确性方面的潜力。研究结果支持在临床环境中整合这些先进的预测工具，以更好地指导患者及其家属的决策、制定个性化的康复计划、有效分配资源，并最终改善患者结局。
