Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions
Summary: arXiv:2604.06214v1 Announce Type: cross
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.
Introduction
In contemporary healthcare settings, the ability to accurately classify surgical procedures based on urgency is essential for effective patient management and resource utilization. Traditional methods of urgency classification often rely on manual assessment, which can be time-consuming and subjective. To address these challenges, this research employs an innovative unsupervised neural network model.
Methodology
This study leverages BioClinicalBERT, a domain-specific language model designed to process medical texts. The surgical transcripts are converted into high-dimensional embeddings that encapsulate their semantic features. The clustering of these embeddings is performed using two primary algorithms:
- K-means: A widely used clustering algorithm that partitions data into distinct groups based on similarity.
- Deep Embedding Clustering (DEC): An advanced technique that enhances clustering quality by simultaneously optimizing the representation and the clustering process.
Preliminary results indicate that DEC significantly outperforms K-means in creating cohesive and well-separated clusters.
Validation
To ensure the clinical relevance and accuracy of the clustering results, the Modified Delphi Method is employed. This method involves a systematic review and refinement process conducted by a panel of medical experts. Their insights help validate the urgency classifications and ensure alignment with clinical practices.
Development of the Classification Model
Following the validation phase, a neural network model is developed that integrates Bidirectional Long Short-Term Memory (BiLSTM) layers with the BioClinicalBERT embeddings. This model is designed specifically for the classification of surgical urgency levels.
Evaluation
The performance of the developed model is rigorously evaluated through cross-validation techniques. Key metrics used in the evaluation include:
- Accuracy: Measures the overall correctness of the model.
- Precision: Indicates the proportion of true positive results in the positive predictions.
- Recall: Reflects the model’s ability to identify all relevant instances.
- F1-score: A harmonic mean of precision and recall that provides a balance between the two metrics.
The results demonstrate robust performance and strong generalization capabilities on unseen data, highlighting the model’s effectiveness in real-world applications.
Conclusion
This unsupervised framework not only addresses the challenge of limited labeled data but also provides a scalable and reliable solution for real-time surgical prioritization. By enhancing operational efficiency and improving patient outcomes, this innovative approach has the potential to transform surgical management in dynamic medical environments.