Estimating Prevalence, Demographics, and Costs of ME/CFS Using Large Scale Medical Claims Data and Machine Learning

• Main Authors: Ashley R. Valdez (Author), Elizabeth E. Hancock (Author), Seyi Adebayo (Author), David J. Kiernicki (Author), Daniel Proskauer (Author), John R. Attewell (Author), Lucinda Bateman (Author), Alfred DeMaria (Author), Charles W. Lapp (Author), Peter C. Rowe (Author), Charmian Proskauer (Author)
• Format: Book
• Published: Frontiers Media S.A., 2019-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Techniques of data mining and machine learning were applied to a large database of medical and facility claims from commercially insured patients to determine the prevalence, gender demographics, and costs for individuals with provider-assigned diagnosis codes for myalgic encephalomyelitis (ME) or c...