Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases

Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases

Abstract Background The healthcare sector is an interesting target for fraudsters. The availability of a great amount of data makes it possible to tackle this issue with the adoption of data mining techniques, making the auditing process more efficient and effective. This research has the objective...

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Bibliographic Details
Main Authors: Michela Carlotta Massi, Francesca Ieva, Emanuele Lettieri
Format: Article
Language:English
Published: BMC 2020-07-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Data mining
Fraud
Upcoding
Administrative database
DRG
Online Access:http://link.springer.com/article/10.1186/s12911-020-01143-9

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http://link.springer.com/article/10.1186/s12911-020-01143-9

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