Transformer Models for Enhanced Natural Language Processing in Medical Records Management

The use of transformer models to enhance NLP in MR administration is investigated. Renowned for their ability to understand contextual links along with efficiently handle large datasets, transformer designs provide clear improvements in the processing along with analysis of medical language. Work improves the extraction, classification, along with summarising of data from complex medical records by use of models like BERT, GPT, along with variants. The study looks at how well these models may simplify data retrieval, increase the accuracy and relevance of CR, along with support decision making in medical environments. The results imply that the use of transformer-based NLP techniques might greatly boost the medical record administration, thereby improving patient care, accelerating procedures, along with optimising data usage. Transformer models such as BERT are used in the research approach aiming at enhancing NLP in medical record handling. Literature, preprocessing EHRs, fine-tuning and assessment along with pilot testing all plays a part. Maintaining ethical issues, a user interface is given for interaction. This method improves medical data handling effectiveness. BERT has showed promise in handling medical records in terms of enhancing identification and categorisation of medical entities found in data. Various medical entities including diseases, medications, procedures, and symptoms have been specifically tailored into the strong language paradigm. Confusion matrix performance measures reveal how well model controls medical records. Future studies could investigate combining BERT with other advanced NLP methods, extending dataset to include a wider range of medical records, using TLT in case of particular medical domains, including real time data processing and feedback systems, and investigating ethical issues along with data privacy concerns in health care.