Natural language processing of German clinical colorectal cancer notes for guideline-based treatment evaluation
Natural language processing of German clinical colorectal cancer notes for guideline-based treatment evaluation
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DOI：
10.1016/j.ijmedinf.2019.04.022
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发表时间：
2019-07-01
影响因子：
4.9
通讯作者：
Virchow, Isabel
中科院分区：
文献类型：
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作者：
Becker, Matthias;Kasper, Stefan;Virchow, Isabel
背景：结直肠癌是德国最常见的癌症，在女性和男性中分别排名第二和第三。这种疾病的治疗主要基于肿瘤分期，通常在医疗信息系统中以非结构化的形式记录。为了重用这些知识，必须使用正确的术语提取和注释信息。方法：在本研究中，开发了一种自然语言处理管道，用于识别基于指南的特定患者信息，并用统一医学语言系统概念对其进行注释，以便医生进行人工评估。一次性评估的黄金标准是通过从电子健康记录中提取2513份德国临床记录来确定的。结果：利用该方法对结直肠癌叙述性临床记录进行回顾性评价，对肿瘤分期检测的准确率为96.64%，对诊断识别的准确率为97.95%，召回率分别为94.89%和99.54%。对于已知癌症诊断的患者（11个概念组），与治疗决策相关的所有概念的平均精度值分别达到82.05%，召回值为82.45%，f1得分为81.81%。结论：从叙述性临床记录中识别基于指南的信息具有作为临床决策支持工具的潜力。
Background: Colorectal cancer is the most commonly occurring cancer in Germany, and the second and third most commonly diagnosed cancer in women and men, respectively. The therapy for this disease is based primarily on the tumor stages, which are usually documented in an unstructured form in medical information systems. In order to re-use this knowledge, the information must be extracted and annotated using the correct terminology.Methods: In this study, a natural language processing pipeline is developed to identify specific guideline-based patient information and to annotate it with Unified Medical Language System concepts for manual evaluation by a physician. The gold standard for one-time evaluation is determined using the human abstraction of 2513 German clinical notes from electronic health records.Results: Using this approach to process the narrative clinical notes on colorectal cancer for retrospective evaluation of the therapy recommendation, the algorithm achieves a precision value of 96.64% for tumor stage detection and 97.95% for diagnosis recognition with recall values of 94.89% and 99.54%, respectively. The average precision value across all concepts relevant to treatment decisions for patients with known cancer diagnoses (11 concept groups) achieved a precision value of 82.05% with a recall value of 82.45% and an F1-score of 81.81%, respectively.Conclusions: The identification of guideline-based information from narrative clinical notes has the potential for implementation as clinical decision support tools.