Eghbali Niloufar, Klochko Chad, Mahdi Zaid, Alhiari Laith, Lee Jonathan, Knisely Beatrice, Craig Joseph, Ghassemi Mohammad M
Michigan State University, East Lansing, MI, USA.
Henry Ford Health, Detroit, MI, USA.
J Imaging Inform Med. 2025 Apr 7. doi: 10.1007/s10278-025-01477-8.
Insufficient clinical information provided in radiology requests, coupled with the cumbersome nature of electronic health records (EHRs), poses significant challenges for radiologists in extracting pertinent clinical data and compiling detailed radiology reports. Considering the challenges and time involved in navigating electronic medical records (EMR), an automated method to accurately compress the text while maintaining key semantic information could significantly enhance the efficiency of radiologists' workflow. The purpose of this study is to develop and demonstrate an automated tool for clinical note summarization with the goal of extracting the most pertinent clinical information for the radiological assessments. We adopted a transfer learning methodology from the natural language processing domain to fine-tune a transformer model for abstracting clinical reports. We employed a dataset consisting of 1000 clinical notes from 970 patients who underwent knee MRI, all manually summarized by radiologists. The fine-tuning process involved a two-stage approach starting with self-supervised denoising and then focusing on the summarization task. The model successfully condensed clinical notes by 97% while aligning closely with radiologist-written summaries evidenced by a 0.9 cosine similarity and a ROUGE-1 score of 40.18. In addition, statistical analysis, indicated by a Fleiss kappa score of 0.32, demonstrated fair agreement among specialists on the model's effectiveness in producing more relevant clinical histories compared to those included in the exam requests. The proposed model effectively summarized clinical notes for knee MRI studies, thereby demonstrating potential for improving radiology reporting efficiency and accuracy.
放射学申请中提供的临床信息不足，再加上电子健康记录（EHRs）的繁琐性质，给放射科医生提取相关临床数据和编写详细的放射学报告带来了重大挑战。考虑到浏览电子病历（EMR）所涉及的挑战和时间，一种在保持关键语义信息的同时准确压缩文本的自动化方法可以显著提高放射科医生的工作流程效率。本研究的目的是开发并展示一种用于临床记录总结的自动化工具，目标是提取用于放射学评估的最相关临床信息。我们采用了自然语言处理领域的迁移学习方法来微调一个用于提取临床报告摘要的变压器模型。我们使用了一个数据集，该数据集由970名接受膝关节MRI检查的患者的1000份临床记录组成，所有记录均由放射科医生手动总结。微调过程采用两阶段方法，首先是自监督去噪，然后专注于总结任务。该模型成功地将临床记录压缩了97%，同时与放射科医生编写的摘要紧密对齐，余弦相似度为0.9，ROUGE-1分数为40.18。此外，统计分析（Fleiss卡帕分数为0.
