基于知识辅助的结构化医疗报告生成

doi:10.11896/jsjkx.230900076

Abstract

Abstract: Automatic generation of medical reports is an important application of text summarization technology.Due to the ob-vious difference between the medical consultation data and data of the general field,the traditional text summary generation me-thod cannot fully understand and utilize the highly complex medical terms in the medical text,so that the key knowledge contained in the medical consultation has not been fully used.In addition,most of the traditional text summary generation methods directly generate summaries,and do not have the ability to automatically select and filter key information and generate structured text according to the structural characteristics of medical reports.In order to solve the above problems,a knowledge-assisted structured medical report generation method is proposed in this paper.The proposed method combines the entity-guided prior domainknowledge with the structure-guided task decoupling mechanism,and realizes the key knowledge of medical consultation data,taking full advantage of the structured features of medical reports.The effectiveness of the method is verified on the IMCS21 dataset.The ROUGE score of the summary generated by our method is 2% to 3％ higher than that of baseline methods,and a more accurate medical report is generated.

Key words: Medical report generation, Pre-training model, Generative summarization, Domain knowledge prior, Task decoupling mechanism

CLC Number:

TP301

SHI Jiyun, ZHANG Chi, WANG Yuqiao, LUO Zhaojing, ZHANG Meihui. Generation of Structured Medical Reports Based on Knowledge Assistance[J].Computer Science, 2024, 51(6): 317-324.

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