用伪标签+混合模型检测心理医疗账单异常，解决标注数据少难题。
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection
- 用孤立森林和自编码器生成伪标签，训练LSTM与Transformer混合模型。
- 在申报级数据上召回率达0.963，操作级数据上达0.744。
- 适合处理标注稀缺、类别不平衡的医疗账单异常检测场景。
心理健康医疗账单的复杂性导致欺诈等异常行为。尽管机器学习已用于异常检测，但仍面临类别不平衡、标注数据稀缺及复杂序列模式等问题。本研究探索一种结合长短期记忆网络（LSTM）与Transformer的混合深度学习方法，通过孤立森林（iForest）和自编码器（AE）进行伪标签生成。现有工作未在医疗账单背景下评估此类基于伪标签的混合模型。该方法在两个真实世界心理医疗账单数据集上进行了评估。iForest-LSTM基线在申报级数据上达到最高召回率（0.963）。在操作级数据上，基于iForest的混合模型取得最高召回率（0.744），但精度较低。结果表明，在复杂且不平衡的异常检测场景中，结合伪标签与混合深度学习具有潜力。
原文摘要 · Abstract (English)
The complexity of mental healthcare billing enables anomalies, including fraud. While machine learning methods have been applied to anomaly detection, they often struggle with class imbalance, label scarcity, and complex sequential patterns. This study explores a hybrid deep learning approach combining Long Short-Term Memory (LSTM) networks and Transformers, with pseudo-labeling via Isolation Forests (iForest) and Autoencoders (AE). Prior work has not evaluated such hybrid models trained on pseudo-labeled data in the context of healthcare billing. The approach is evaluated on two real-world billing datasets related to mental healthcare. The iForest LSTM baseline achieves the highest recall (0.963) on declaration-level data. On the operation-level data, the hybrid iForest-based model achieves the highest recall (0.744), though at the cost of lower precision. These findings highlight the potential of combining pseudo-labeling with hybrid deep learning in complex, imbalanced anomaly detection settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。