文献类型：期刊文献

英文题名：Automated Clinical Summary Generation via Integrating Structured andUnstructured Data

作者：Fu, Jiaojiao[1]; Yang, Bowen[2,3]; Guo, Yi[1]; Zhou, Yangfan[2,3]; Wang, Xin[2,3]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] Shanghai Key Lab. of Intelligent Information Processing, Fudan University, Shanghai, China; [3] School of Computer Science, Fudan University, Shanghai, China

年份：2025

卷号：2301

起止页码：290

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20250917946761)

语种：英文

外文关键词：Electronic health record

摘要：Automatically generating clinical texts can significantly reduce the time physicians spend on clinical data recording, which is particularly important for developing countries where physicians are extremely busy due to a severe shortage. This work automatically generates discharge summaries as a case to explore the methods and feasibilities of automatic clinical text summarization. Existing work typically uses either structured or unstructured data alone to generate discharge summaries. However, the content generated often has issues such as being overly verbose, lacking focus, or omitting significant information, especially key indicators and medications. This work innovatively proposes a data integration-based clinical text generation approach, using content generated from unstructured clinical data as the basis and supplementing it with text generated from structured clinical data. This study utilizes advanced natural language processing algorithms and models to create clinical texts. It addresses the challenges of lacking datasets suitable for fine-tuning pre-trained models and combining the advantages rather than the disadvantages of both types to produce discharge summaries. Experimental results show that the structured supplementation approach can effectively improve the generation of clinical texts. This work demonstrates that clinical texts generated using existing natural language processing technologies still do not meet the demands of medical practice, pointing out the need to develop further text generation technologies tailored to the characteristics of clinical data. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.