Enhanced NLP for Medical Text Classification: A Deep Active Learning Approach

This paper presents an enhanced approach for classifying medical texts, combining Deep Active Incremental Learning (AIL) with state-of-the-art techniques to optimize healthcare authorization decisions. Using a Bi-LSTM architecture that is enhanced with contextual embedding and attention mechanisms, the model can dynamically learn from a few labeled data and update its predictions in real-time via entropy-based uncertainty sampling. The proposed framework adopted SMOTE and undersampling strategies. 117,000 actual medical authorization submissions were semantically processed through BioBERT embeddings and Named Entity Recognition (NER). The experimental results show that after 100 active phases of learning, the model achieved a gain of 4% balanced accuracy, indicating its ability to iteratively optimize predictions with minimal guidance. Through the optimization of performance in a constrained resource environment, this approach also enables faster and more efficient processing of medical claims, which can help build scalable and adaptive decision-making capacities.

• Publication year

  2025

• Venue

  Engineering, Technology & Applied Science Research

• Publication date

  2025-10-06

• Fields of study

  Not labeled

• Identifiers
  DOI 10.48084/etasr.12114
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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