标题： 探索一种混合深度学习方法用于心理健康保健提供者的异常检测：通过半监督异常检测解决标签稀缺问题 显示英文标题

标题： Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection

摘要： 心理健康医疗账单的复杂性导致了异常情况，包括欺诈行为。 虽然已经应用了机器学习方法来进行异常检测，但它们在处理类别不平衡、标签稀缺和复杂的序列模式方面常常遇到困难。 本研究探讨了一种混合深度学习方法，结合长短期记忆（LSTM）网络和Transformer，通过孤立森林（iForest）和自编码器（AE）进行伪标记。 以前的工作尚未在医疗账单背景下评估过在伪标记数据上训练的此类混合模型。 该方法在两个与心理健康医疗相关的实际账单数据集上进行了评估。 iForest LSTM基线在声明级数据上达到了最高的召回率（0.963）。 在操作级数据上，基于混合iForest的模型达到了最高的召回率（0.744），尽管是以较低的精确率为代价。 这些发现突显了在复杂且不平衡的异常检测环境中，将伪标记与混合深度学习相结合的潜力。 显示英文摘要

摘要： 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.