Named Entity Recognition in Clinical Domain

Abstract

Named Entity Recognition (NER) plays a fundamental role in extracting valuable information from unstructured clinical and bioinformatic text data. In the context of clinical and bioinformatic research, NER algorithms are essential for identifying and categorizing specific entities such as genes, proteins, diseases, medications, and clinical parameters from extensive textual sources. The clinical sector heavily relies on electronic health records (EHRs) and medical literature, which contain a wealth of patient information. NER aids in automating the extraction of crucial patient-specific details, enabling healthcare professionals to make informed decisions, improve patient care, and support clinical research. In bioinformatics, it assists in the analysis of genomic and proteomic data by accurately identifying genes, proteins, and their interactions in scientific literature. However, NER in these domains faces various challenges, including the ambiguity of entity mentions, variations in terminology, and domain-specific jargon. Recent advancements in deep learning techniques, such as transformer-based models, have shown significant performance improvements in NER tasks in clinical and bioinformatic contexts. These models leverage pre-trained representations and can be fine-tuned on domain-specific datasets to achieve state-of-the-art results. Additionally, the availability of large-scale annotated corpora and open-source tools has made NER more accessible to researchers and practitioners in these fields. In conclusion, NER is a crucial component in the extraction of structured information from unstructured text data in clinical and bioinformatic domains. Despite the challenges, recent advancements in machine learning and natural language processing techniques have significantly enhanced NER׳s accuracy and efficiency, opening up new possibilities for knowledge extraction and discovery in these critical domains.