J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (4): 778-789.doi: 10.1007/s12204-025-2825-5
收稿日期:
2024-12-02
接受日期:
2025-02-25
出版日期:
2025-07-31
发布日期:
2025-07-31
宓林晖1,袁骏毅1,周延康2,侯旭敏3
Received:
2024-12-02
Accepted:
2025-02-25
Online:
2025-07-31
Published:
2025-07-31
摘要: 手术部位感染是肺癌患者中最常见的医疗相关感染。构建肺癌手术部位感染的风险预测模型需要从肺癌病例文本中提取相关风险因素,这涉及两种类型的文本结构化任务:属性判别和属性提取。围绕这两种任务提出了一种联合模型,即Multi BGLC;该模型使用BERT作为编码器,并基于癌症病例数据,对由GCNN+LSTM+CRF组成的解码器进行微调。其中,GCNN用于属性判别,而LSTM和CRF用于属性提取。实验证明,与其他基线模型相比,该模型的有效性和准确性更高。
中图分类号:
. 基于深度学习的肺癌病例文本结构化算法[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(4): 778-789.
Mi Linhui, Yuan Junyi, Zhou Yankang, Hou Xumin. Text Structured Algorithm of Lung Cancer Cases Based on Deep Learning[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(4): 778-789.
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