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.
Other autoimmune disorders and specific cancers have been proposed as potential comorbidities to MS. In a paper that summarized the environmental features researched in etiological research on MS @cite_9 , Lauer noted that inflammatory bowel disease (IBD), ulcerative colitis, and Type 1 diabetes have the strongest correlations to MS amongst the various autoimmune disorders. The paper also referenced potential associations with Hodgkin's, oral, and colon cancers with the caveat that there was insufficient evidence to support these connections.
@cite_9
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The etiology of multiple sclerosis is, at present, not definitely known, but genetic and environmental factors play a role in its causation. Environmental causes have a predominant impact. Epidemiologic research has contributed considerably to the identification of external risk factors in this multifactorial setting, but methodological constraints still play a major part. Viral and other microbial agents have drawn much attention, although none of them is a necessary condition for the disease. This is true also for the Epstein–Barr virus, for which most data, including prospective data, supports a role in the majority of multiple sclerosis patients. In parallel, the hypothesis is still attractive in that it is not the virus per se, but rather more the age when it infects the human being that is the crucial matter. Other risk factors, such as tobacco smoking and vitamin D deficiency, which have immunomodulating properties, may also play some role, although the latter is not compatible with all data of the...