Pseudo-labeling with Isolation Forest lets an LSTM detect billing anomalies with high recall, but the hybrid LSTM-Transformer adds little and reduces precision.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Exploring a Hybrid Deep Learning Approach for Anomaly Detection in Mental Healthcare Provider Billing: Addressing Label Scarcity through Semi-Supervised Anomaly Detection
Pseudo-labeling with Isolation Forest lets an LSTM detect billing anomalies with high recall, but the hybrid LSTM-Transformer adds little and reduces precision.