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

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

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...

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Bibliographic Details
Main Authors: Ashley R. Valdez, Elizabeth E. Hancock, Seyi Adebayo, David J. Kiernicki, Daniel Proskauer, John R. Attewell, Lucinda Bateman, Alfred DeMaria, Charles W. Lapp, Peter C. Rowe, Charmian Proskauer
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-01-01
Series:Frontiers in Pediatrics
Subjects:
ME/CFS
myalgic encephalomyelitis
chronic fatigue syndrome
prevalence
costs
machine learning
Online Access:https://www.frontiersin.org/article/10.3389/fped.2018.00412/full

Internet

https://www.frontiersin.org/article/10.3389/fped.2018.00412/full

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