TY - JOUR AU - Han, AU - jin Fu，孙阳AU - Kolis, Julie AU - Hughes, Richard AU - Hallstrom, Brian R AU - Carvour, Martha AU - Maradit-Kremers, Hilal AU - Sohn, Sunghwan AU - Vydiswaran, VG Vinod PY - 2022 DA - 2022/8/31 TI -全髋关节置换术中常见数据元素检测的自然语言处理算法的多中心验证:算法开发和验证JO - JMIR Med Inform SP - e38155 VL - 10 IS - 8 KW -全髋关节置换术KW -自然语言处理KW -信息提取KW -模型可移植性AB -背景:自然语言处理(NLP)方法是从自由文本数据中提取和分析关键信息的强大工具。MedTaggerIE是一个基于文本模式的信息提取的开源NLP管道，已广泛应用于临床笔记的注释。基于MedTaggerIE开发的基于规则的系统，medtagger -全髋关节置换术(THA)，先前在梅奥诊所被证明可以从THA手术记录中正确识别手术入路、固定和承受面。目的:本研究旨在评估MedTagger-THA在密歇根医学院和爱荷华大学两所外部机构的可实施性、可用性和可移植性，并为最佳实践提供经验教训。方法:我们使用三个相关的站点——开发站点(梅奥诊所)和两个部署站点(密歇根医学和爱荷华大学)来执行迭代测试-应用-精化过程。梅奥诊所是主要的NLP开发地点，以THA注册为黄金标准。两个部署地点的活动包括摘录执行部分说明、制订金标准(密歇根:登记数据;爱荷华州:手工图表审查)，NLP算法对训练数据的改进，以及测试数据的评估。进行了错误分析，以了解不同地点的语言差异。 To further assess the model specificity for approach and fixation, we applied the refined MedTagger-THA to arthroscopic hip procedures and periacetabular osteotomy cases, as neither of these operative notes should contain any approach or fixation keywords. Results: MedTagger-THA algorithms were implemented and refined independently for both sites. At Michigan, the study comprised THA-related notes for 2569 patient-date pairs. Before model refinement, MedTagger-THA algorithms demonstrated excellent accuracy for approach (96.6%, 95% CI 94.6%-97.9%) and fixation (95.7%, 95% CI 92.4%-97.6%). These results were comparable with internal accuracy at the development site (99.2% for approach and 90.7% for fixation). Model refinement improved accuracies slightly for both approach (99%, 95% CI 97.6%-99.6%) and fixation (98%, 95% CI 95.3%-99.3%). The specificity of approach identification was 88.9% for arthroscopy cases, and the specificity of fixation identification was 100% for both periacetabular osteotomy and arthroscopy cases. At the Iowa site, the study comprised an overall data set of 100 operative notes (50 training notes and 50 test notes). MedTagger-THA algorithms achieved moderate-high performance on the training data. After model refinement, the model achieved high performance for approach (100%, 95% CI 91.3%-100%), fixation (98%, 95% CI 88.3%-100%), and bearing surface (92%, 95% CI 80.5%-97.3%). Conclusions: High performance across centers was achieved for the MedTagger-THA algorithms, demonstrating that they were sufficiently implementable, usable, and portable to different deployment sites. This study provided important lessons learned during the model deployment and validation processes, and it can serve as a reference for transferring rule-based electronic health record models. SN - 2291-9694 UR - https://medinform.www.mybigtv.com/2022/8/e38155 UR - https://doi.org/10.2196/38155 UR - http://www.ncbi.nlm.nih.gov/pubmed/36044253 DO - 10.2196/38155 ID - info:doi/10.2196/38155 ER -