BACKGROUND Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique. METHODS In this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports. RESULTS Evaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively. CONCLUSION This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.

中文翻译：

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用于填充肺癌临床研究数据的自然语言处理。

背景技术肺癌是男女第二大最常见的癌症。电子健康记录（EHR）的广泛采用为利用信息学方法加速与队列相关的流行病学研究提供了潜力。由于从大量文本材料中手动提取是费时且费力的工作，因此出现了一些努力，以使用人工智能技术自然语言处理（NLP）从肺癌患者的文本中自动提取信息。方法在这项研究中，我们使用现有的2311名肺癌患者队列，并手动确定了有关分期，组织学，肿瘤级别和疗法（化学疗法，放射疗法和外科手术）的信息，我们开发并评估了NLP系统，以从临床叙述（包括临床笔记，病理报告和手术报告）中自动为同一患者提取有关这些变量的信息。结果评估显示出令人鼓舞的结果，分期，组织学，肿瘤分级和治疗的召回率分别达到89％，98％，78％和100％，准确率分别为70％，88％，90％和100％。结论本研究证明了从临床研究中自动提取预定义信息以进行肺癌研究的可行性和准确性。和100％。结论本研究证明了从临床研究中自动提取预定义信息以进行肺癌研究的可行性和准确性。和100％。结论本研究证明了从临床研究中自动提取预定义信息以进行肺癌研究的可行性和准确性。