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Addressing Label Scarcity: Hybrid Anomaly Detection in Mental Healthcare Billing

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Samirah Bakker
Yao Ma
Seyed Sahand Mohammadi Ziabari
Published in
Information Integration and Web Intelligence
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the challenges of detecting anomalies in mental healthcare billing data, particularly focusing on the issue of label scarcity. It explores the use of hybrid deep learning models, specifically combining LSTM networks and Transformer architectures, to improve anomaly detection in imbalanced datasets. The study employs pseudo-labeling strategies using unsupervised anomaly detection techniques such as Isolation Forests and Autoencoders to generate synthetic labels, thereby enriching the minority class. The performance of these models is evaluated on two real-world datasets, with the iForest LSTM model achieving the highest accuracy. The chapter also discusses the limitations of traditional supervised learning techniques in highly imbalanced data scenarios and highlights the importance of model architecture and labeling strategies in anomaly detection. The findings suggest that simpler sequential models may be more effective in practical deployment, offering valuable insights for professionals in the field of fraud detection and data analysis.
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previous chapter Japanese Rhyme Generation Based on Mora Similarity and Generation Probability
next chapter BATT2GRAPH: A Hybrid CNN-LSTM and Temporal Graph-Based Approach for Lithium-Ion Battery SOH Prediction and Anomaly Detection
Title
Addressing Label Scarcity: Hybrid Anomaly Detection in Mental Healthcare Billing
Authors
Samirah Bakker
Yao Ma
Seyed Sahand Mohammadi Ziabari
Copyright Year
2026
Book
Information Integration and Web Intelligence

Print ISBN: 978-3-032-11975-9

Electronic ISBN: 978-3-032-11976-6

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-11976-6_8
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