Leveraging Named Entity Recognition for Enhanced Meningioma Management: Integrating Imaging and Personalized Medicine Data

Manli Du Du^*

• Dalian Medical University, Dalian, China

The integration of advanced computational techniques into the management of meningioma has become increasingly vital, particularly in the context of personalized medicine and sophisticated imaging modalities. As diagnostic and treatment paradigms evolve, there is a growing emphasis on leveraging high-throughput data and artificial intelligence to inform clinical decision-making. Despite the proliferation of biomedical literature and clinical data, extracting actionable insights remains a significant challenge. The complexity of unstructured data sources, such as electronic health records, pathology reports, and scientific publications, introduces substantial variability in terminology and context. Traditional Named Entity Recognition (NER) methods, often reliant on rule-based systems or conventional machine learning algorithms, struggle with the complexity and variability inherent in medical texts. These approaches frequently lack the adaptability required to accurately identify and classify the diverse range of entities pertinent to meningioma research and treatment, including tumor subtypes, anatomical locations, genetic markers, and therapeutic interventions. Moreover, the dynamic nature of medical knowledge necessitates NER systems that can evolve with emerging scientific insights. Recent advancements in deep learning and transformer-based language models offer promising alternatives by enabling context-aware recognition and improved generalization across varied datasets. Integrating such models into biomedical pipelines could significantly enhance the extraction of meaningful information, ultimately facilitating more precise and individualized approaches to meningioma care.