Liver Cancer Knowledge Graph Construction based on dynamic entity replacement and masking strategies RoBERTa-BiLSTM-CRF model
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ID: 282816
2024
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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%.
| Reference Key |
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| Authors | YiChi Zhang; HaiLing Wang; YongBin Gao; XiaoJun Hu; YingFang Fan; ZhiJun Fang |
| Journal | arXiv |
| Year | 2024 |
| DOI |
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