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This article discusses the use of machine learning algorithms to develop a predictive model for determining ambulatory status in spinal cord injury patients one year post-injury. The study found that Elastic Net Penalized Logistic Regression (ENPLR) was the most accurate model, outperforming traditional models and other machine learning methods. The use of artificial intelligence and machine learning in this context could potentially improve sensitivity in identifying individuals who may benefit from additional strategies for ambulation.
