Machine learning and the future of Medicare fraud detection

J Am Acad Dermatol. 2020 Aug;83(2):e133. doi: 10.1016/j.jaad.2020.03.059. Epub 2020 Mar 29.

Authors

Dan Gordon¹, Daniel M Siegel²

Affiliations

¹ Department of Dermatology, State University of New York Downstate Health Sciences University, Brooklyn. Electronic address: dan.gordon@downstate.edu.
² Department of Dermatology, State University of New York Downstate Health Sciences University, Brooklyn.

PMID: 32234308
DOI: 10.1016/j.jaad.2020.03.059

No abstract available

Publication types

Letter

MeSH terms

Administrative Claims, Healthcare / economics
Administrative Claims, Healthcare / statistics & numerical data
Clinical Coding / economics
Clinical Coding / statistics & numerical data
Data Mining / methods*
Data Mining / trends
Datasets as Topic
Financial Audit / methods*
Financial Audit / trends
Fraud / prevention & control*
Machine Learning*
Medicare / economics*
Medicare / statistics & numerical data
United States