Neural network applications in physical medicine and rehabilitation.
The purpose of this article is to provide an overview of neural networks and their applications in physical medicine and rehabilitation. Conventional statistical models may present certain limitations that can be overcome by neural networks. We show what neural networks are, how they "learn" regularities from the data, and how they can classify previously unseen cases. We present advantages and disadvantages of using neural networks and compare them with regression models. We explain how neural networks can be used as statistical tools for making inferences using the example of a prognostic model that predicts ambulation after spinal cord injury.
Decision Systems Group, Brigham and Women's Hospital, Health Science and Technology Division, Harvard Medical School, Boston, Massachusetts 02115, USA.
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Ohno-Machado
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Rowland
D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010810:Physical and Rehabilitation Medicine / Q000379:methods*; D011379:Prognosis; D012044:Regression Analysis; D012046:Rehabilitation / Q000379:methods*; D013119:Spinal Cord Injuries / Q000175:diagnosis