Wang Liwei, Luo Lei, Wang Yanshan, Wampfler Jason A, Yang Ping, Liu Hongfang
Department of health Sciences Research Mayo Clinic, Rochester, MN, U.S.
Department of Good Clinical Practice Guizhou Province People's Hospital Guiyang, Guizhou, China.
Proc (IEEE Int Conf Healthc Inform). 2019 Jun;2019. doi: 10.1109/ICHI.2019.8904601. Epub 2019 Nov 21.
Lung cancer is the second most common cancer and the wide adoption of electronic health records (EHRs) offers a potential of accelerating cohort-related epidemiological studies using informatics approaches. In this study, we developed and evaluated a natural language processing (NLP) system to extract information on stage, histology, grade and therapies (chemotherapy, radiotherapy and surgery) automatically for lung cancer patients from clinical narratives including clinical notes, pathology reports and surgery reports. Evaluation showed promising results with the recalls for stage, histology, grade, and therapies achieving 89%, 98%, 80%, and 100% respectively and the precisions were 71%, 89%, 90%, and 100% respectively. This study demonstrated the feasibility and accuracy of extracting related information from clinical narratives for lung cancer research.
肺癌是第二大常见癌症，电子健康记录（EHRs）的广泛应用为使用信息学方法加速队列相关的流行病学研究提供了潜力。在本研究中，我们开发并评估了一个自然语言处理（NLP）系统，用于从包括临床记录、病理报告和手术报告在内的临床叙述中自动提取肺癌患者的分期、组织学、分级和治疗（化疗、放疗和手术）信息。评估显示出有前景的结果，分期、组织学、分级和治疗的召回率分别达到89%、98%、80%和100%，精确率分别为71%、89%、90%和100%。本研究证明了从临床叙述中提取相关信息用于肺癌研究的可行性和准确性。
