Tumor Event Extraction based on Semantic Features and Its Application in Auxiliary Diagnosis and Treatment_Vol. 4 No. 2 (JBDC 2026)_Journal of Big Data and Computing (ISSN: 2959-0590)

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Tumor Event Extraction based on Semantic Features and Its Application in Auxiliary Diagnosis and Treatment

DOI: https://doi.org/10.62517/jbdc.202601210

Author(s)

Xiaoxue Li*, Bairu Jia, Yao Liu

Affiliation(s)

School of Information Engineering, Shaanxi University of International Trade & Commerce, Xi’an, Shaanxi, China * Corresponding Author

Abstract

To address the challenge of utilizing unstructured text in tumor electronic medical records for clinical decision-making, this study proposes a medical event extraction method based on fused semantic features and designs an oncology-assisted diagnosis and treatment visualization system. First, a pre-trained medical event extraction model is employed to automatically extract key event information from tumor records, including tumor type, lesion size, location, grade, and metastasis. Second, an auxiliary diagnosis and treatment system is developed, incorporating functions such as tumor grading, metastasis detection, personalized treatment plan formulation, prognosis evaluation, and data statistical analysis. Finally, a user interface is designed using Qt Designer to visualize the workflow of record input, model invocation, diagnostic recommendation generation, and evaluation. Practical applications demonstrate that this system effectively assists clinicians in rapidly extracting critical information from records and generating reasonable treatment plans, enhancing the efficiency and interpretability of tumor diagnosis and treatment while validating the feasibility of medical event extraction technology in clinical oncology assistance.

Keywords

Tumor Event Extraction; Semantic Feature Fusion; Auxiliary Diagnosis and Treatment; Electronic Medical Records; Visualization System.

References

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