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Abstract：
Background: Prediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Artificial neural networks (ANNs) have emerged as a promising alternative to conventional statistical approaches for identifying complex prognostic factors in SCI patients. Materials: a database of 1256 SCI patients admitted for rehabilitation was analyzed. Clinical and demographic data and SCI characteristics were used to predict functional outcomes using both ANN and linear regression models. The former was structured with input, hidden, and output layers, while the linear regression identified significant variables affecting outcomes. Both approaches aimed to evaluate and compare their accuracy for rehabilitation outcomes measured by the Spinal Cord Independence Measure (SCIM) score. Results: Both ANN and linear regression models identified key predictors of functional outcomes, such as age, injury level, and initial SCIM scores (correlation with actual outcome: R = 0.75 and 0.73, respectively). When also alimented with parameters recorded during hospitalization, the ANN highlighted the importance of these additional factors, like motor completeness and complications during hospitalization, showing an improvement in its accuracy (R = 0.87). Conclusions: ANN seemed to be not widely superior to classical statistics in general, but, taking into account complex and non-linear relationships among variables, emphasized the impact of complications during the hospitalization on recovery, particularly respiratory issues, deep vein thrombosis, and urological complications. These results suggested that the management of complications is crucial for improving functional recovery in SCI patients.
摘要：
背景：脊髓损伤（SCI）后神经康复结果的预测对于医疗保健资源管理以及改善预后和康复策略至关重要。人工神经网络（ANN）已成为传统统计方法的有希望的替代方法，用于识别SCI患者的复杂预后因素。材料：分析了1256例接受康复治疗的SCI患者的数据库。使用ANN和线性回归模型，使用临床和人口统计学数据以及SCI特征来预测功能结果。前者是用输入结构的，隐藏,和输出层，而线性回归确定了影响结果的重要变量。两种方法都旨在评估和比较其通过脊髓独立性测量（SCIM）评分测量的康复结果的准确性。结果：人工神经网络和线性回归模型都确定了功能结果的关键预测因子，比如年龄,损伤水平,和初始SCIM评分（与实际结果的相关性：分别为R=0.75和0.73）。当住院期间记录参数时，人工神经网络强调了这些额外因素的重要性，比如住院期间的运动完全性和并发症，显示其精度提高(R=0.87)。结论：总体上，人工神经网络似乎并不广泛优于经典统计学，但是,考虑到变量之间的复杂和非线性关系，强调住院期间并发症对康复的影响，尤其是呼吸问题，深静脉血栓形成,泌尿系统并发症.这些结果表明，并发症的处理对于改善SCI患者的功能恢复至关重要。
