  Multiple sclerosis (MS) is a chronic autoimmune disease that affects the
central nervous system. The progression and severity of MS varies by
individual, but it is generally a disabling disease. Although medications have
been developed to slow the disease progression and help manage symptoms, MS
research has yet to result in a cure. Early diagnosis and treatment of the
disease have been shown to be effective at slowing the development of
disabilities. However, early MS diagnosis is difficult because symptoms are
intermittent and shared with other diseases. Thus most previous works have
focused on uncovering the risk factors associated with MS and predicting the
progression of disease after a diagnosis rather than disease prediction. This
paper investigates the use of data available in electronic medical records
(EMRs) to create a risk prediction model; thereby helping clinicians perform
the difficult task of diagnosing an MS patient. Our results demonstrate that
even given a limited time window of patient data, one can achieve reasonable
classification with an area under the receiver operating characteristic curve
of 0.724. By restricting our features to common EMR components, the developed
models also generalize to other healthcare systems.
