Real-Time Named Entity Recognition from Textual Electronic Clinical Records in Cancer Therapy Using Low-Latency Neural Networks.

Named entity recognition (NER) is a core task in natural language processing that identifies and classifies entities, such as people, organizations, and locations within text. It has traditionally been applied in areas like text summarization, machine translation, and question answering. In recent years, NER has gained growing importance in health care, where electronic clinical records and online platforms generate large amounts of unstructured medical data. However, applying NER in clinical contexts introduces unique challenges due to the complexity of medical terminology and the need for high accuracy. In this study, we focused on the development of a real-time, low-latency NER system designed for cross-lingual speech-to-text applications, with a particular emphasis on cancer therapy-related clinical records and traditional Chinese medicine (TCM). We explored the integration of deep learning (DL) architectures optimized for low-latency neural processing to extract structured information from multilingual spoken content in medical settings, particularly in multimodal environments. We evaluate DL-based methods and propose a semi-supervised approach that combines TCM-specific corpora with biomedical resources to improve recognition accuracy. The findings provide both a systematic review of current methods and practical insights for building real-time clinical applications that support decision-making and information management in health care.