Liver Cancer Knowledge Graph Construction based on dynamic entity replacement and masking strategies RoBERTa-BiLSTM-CRF model

TL;DR

This paper presents a novel approach to constructing a liver cancer knowledge graph using dynamic entity replacement and masking strategies, improving entity recognition accuracy for aiding diagnosis.

Contribution

It introduces a new DERM strategy for named entity recognition and details a comprehensive process for building a liver cancer knowledge graph from medical data.

Findings

Knowledge graph includes 7 entity types with 1495 entities.

Entity recognition accuracy achieved 93.23%.

The system supports improved diagnosis and treatment planning.

Abstract

Background: Liver cancer ranks as the fifth most common malignant tumor and the second most fatal in our country. Early diagnosis is crucial, necessitating that physicians identify liver cancer in patients at the earliest possible stage. However, the diagnostic process is complex and demanding. Physicians must analyze a broad spectrum of patient data, encompassing physical condition, symptoms, medical history, and results from various examinations and tests, recorded in both structured and unstructured medical formats. This results in a significant workload for healthcare professionals. In response, integrating knowledge graph technology to develop a liver cancer knowledge graph-assisted diagnosis and treatment system aligns with national efforts toward smart healthcare. Such a system promises to mitigate the challenges faced by physicians in diagnosing and treating liver cancer. Methods: This paper addresses the major challenges in building a knowledge graph for hepatocellular carcinoma diagnosis, such as the discrepancy between public data sources and real electronic medical records, the effective integration of which remains a key issue. The knowledge graph construction process consists of six steps: conceptual layer design, data preprocessing, entity identification, entity normalization, knowledge fusion, and graph visualization. A novel Dynamic Entity Replacement and Masking Strategy (DERM) for named entity recognition is proposed. Results: A knowledge graph for liver cancer was established, including 7 entity types such as disease, symptom, and constitution, containing 1495 entities. The recognition accuracy of the model was 93.23%, the recall was 94.69%, and the F1 score was 93.96%.