Developing an anomaly detection framework for Medicare claims
Author:
Affiliation:
1. University of New South Wales, Australia
2. Australian Government Department of Health and Aged Care, Australia
3. Commonwealth Scientific and Industrial Research Organisation, Australia
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3579375.3579410
Reference23 articles.
1. Fraud detection system: A survey
2. C. Aggarwal . 2017. Outlier Analysis ( 2 nd Edition). Springer , New York, USA. C. Aggarwal. 2017. Outlier Analysis (2nd Edition). Springer, New York, USA.
3. A Systematic Review and Qualitative Assessment of Fraud Detection Methodologies in Health Care
4. Real-time big data processing for anomaly detection: A Survey
5. Low-value care in Australian public hospitals: prevalence and trends over time
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1. Topic Modelling and Interpretable Cost Estimation for Medical Insurance Fraud Detection;Communications in Computer and Information Science;2024
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