Explainable unsupervised anomaly detection for healthcare insurance data

Explainable unsupervised anomaly detection for healthcare insurance data

Abstract Background Waste and fraud are important problems for health insurers to deal with. With the advent of big data, these insurers are looking more and more towards data mining and machine learning methods to help in detecting waste and fraud. However, labeled data is costly and difficult to a...

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Bibliographic Details
Main Authors: Hannes De Meulemeester, Frank De Smet, Johan van Dorst, Elise Derroitte, Bart De Moor
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Health insurance
Anomaly detection
Unsupervised machine learning
Online Access:https://doi.org/10.1186/s12911-024-02823-6
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