Transformer-based active learning for multi-class text annotation and classification

Objective: Data-driven methodologies in healthcare necessitate labeled data for effective decision-making. However, medical data, particularly in unstructured formats, such as clinical notes, often lack explicit labels, making manual annotation challenging and tedious.

Methods: This paper introduces a novel deep active learning framework designed to facilitate the annotation process for multiclass text classification, specifically using the SOAP (subjective, objective, assessment, plan) framework, a widely recognized medical protocol. Our methodology leverages transformer-based deep learning techniques to automatically annotate clinical notes, significantly easing the manual labor involved and enhancing classification performance. Transformer-based deep learning models, with their ability to capture complex patterns in large datasets, represent a cutting-edge approach for advancing natural language processing tasks.

Results: We validate our approach through experiments on a diverse set of clinical notes from publicly available datasets, comprising over 426 documents. Our model demonstrates superior classification accuracy, with an F1 score improvement of 4.8% over existing methods but also provides a practical tool for healthcare professionals, potentially improving clinical documentation practices and patient care.

Conclusions: The research underscores the synergy between active learning and advanced deep learning, paving the way for future exploration of automatic text annotation and its implications for clinical informatics. Future studies will aim to integrate multimodal data and large language models to enhance the richness and accuracy of clinical text analysis, opening new pathways for comprehensive healthcare insights.