[2604.06214] Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions

Computer Science > Computation and Language arXiv:2604.06214 (cs) [Submitted on 16 Mar 2026] Title:Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions Authors:Sadaf Tabatabaee, Sarah S. Lam View a PDF of the paper titled Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions, by Sadaf Tabatabaee and 1 other authors View PDF Abstract:Efficient classification of surgical procedures by urgency is paramount to optimize patient care and resource allocation within healthcare systems. This study introduces an unsupervised neural network approach to automatically categorize surgical transcriptions into three urgency levels: immediate, urgent, and elective. Leveraging BioClinicalBERT, a domain-specific language model, surgical transcripts are transformed into high-dimensional embeddings that capture their semantic nuances. These embeddings are subsequently clustered using both K-means and Deep Embedding Clustering (DEC) algorithms, in which DEC demonstrates superior performance in the formation of cohesive and well-separated clusters. To ensure clinical relevance and accuracy, the clustering results undergo validation through the Modified Delphi Method, which involves expert review and refinement. Following validation, a neural network that integrates Bidirectional Long Short-Term Memory (BiLSTM) layers with BioClinicalBERT embeddings is developed for classification task...