Medical Data NER and Classification Using Hybridized BERT Model

Abstract

The extraction of important information from medical texts by Named Entity Recognition (NER) is a key component of advanced medical text processing. Medical practitioners rely heavily on NER's assistance with disease surveillance, clinical resolution building, and substantiation-based treatment. As the foundation of text information processing in the medical field, it guarantees precise location of data required for knowledgeable medical decisions and attentive disease surveillance. Additionally, a core goal in medical Natural Language Processing (NLP) is medical text categorization, which tries to classify short medical texts into distinct groups. Most recent work has concentrated on using pre-trained linguistic processes for text cataloging in medicine. The present work presents a novel clinical neural network architecture (NER) method that was created with a customized Rule Based BiLSTM-BERT (Bidirectional Encoder Representations from Transformers) architecture that incorporates Retrieval Augment Generation. Across several fields, including medicine, deep learning has demonstrated noteworthy advancements. These results show that, when applied to our test dataset, the BiLSTM-BERT-RAG model produced results that were almost human-like. The system proficiently recognized pertinent vocabulary representative of the intended protocol.