Unleashing the Power of NLP and Transformers: A Game-Changer in Medical Research and Clinical Practice and a revolution of Medical Text Analysis.: Case Study: Cancer report classification by priority

The integration of (NLP) and transformers, such as BERT, in the medical field has revolutionized text processing and analysis, offering remarkable opportunities for advancements in research and clinical practice. This article presents a comprehensive exploration of the application of NLP and transformers in the medical domain, specifically focusing on their utilization in cancer-related radiological reports' classification by priority.

The study employs BERT, a bidirectional transformer-based language model, to convert radiological reports into embedding vectors, capturing contextual relationships between words. An attention technique is employed to aggregate word-embedding vectors, emphasizing important words within the report. next, an analysis using the gated rectified unit (GRU) is performed on a collection of radiological reports pertaining to an individual patient. This analysis aims to derive a patient embedding vector, which encapsulates the essential information from the multiple reports and represents the overall condition of the patient. Finally, a classifier is implemented to classify the reports based on their priority levels, enabling doctors to treat patients in an ordered and timely manner. Experimental results on a dataset consisting of 1,000 cancer-related radiological reports demonstrate the efficacy of the proposed approach. The dataset includes reports with varying degrees of severity, allowing for the classification of reports into priority categories. Evaluation metrics such as precision, recall, and F1 score showcase the model's accuracy and robustness, achieving high performance across different priority levels.