Natural Language Processing for Analyzing Neurological Status in Patients With Multiple Sclerosis

Multiple sclerosis is one of the most important diseases affecting the health care system in the modern era. Multiple sclerosis patients' diagnosis and management can be significantly improved with the use of instrumental diagnostic methods, such as magnetic resonance imaging. Based on the diagnostic value of clinical picture data and the unique content of neurological status, the task of learning how to analyze textual sources of a patient's clinical picture arises. To accomplish this task, a model for analyzing textual neurological status was developed, configured, and tested. In developing the model, suitable textual data were collected, exploratory analysis of these data was performed, possible implementations of the neurological status analysis model were analyzed, and the most appropriate option was selected. Currently, the neurological status model is two text data classifiers with prediction of neurological exam score and multiple sclerosis course activity, which have performed well in testing.