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首页> 外文期刊>Journal of biomedical informatics. >Tracking medical students' clinical experiences using natural language processing.
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Tracking medical students' clinical experiences using natural language processing.

机译：使用自然语言处理跟踪医学生的临床经验。

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摘要

Graduate medical students must demonstrate competency in clinical skills. Current tracking methods rely either on manual efforts or on simple electronic entry to record clinical experience. We evaluated automated methods to locate 10 institution-defined core clinical problems from three medical students' clinical notes (n=290). Each note was processed with section header identification algorithms and the KnowledgeMap concept identifier to locate Unified Medical Language System (UMLS) concepts. The best performing automated search strategies accurately classified documents containing primary discussions to the core clinical problems with area under receiver operator characteristic curve of 0.90-0.94. Recall and precision for UMLS concept identification was 0.91 and 0.92, respectively. Of the individual note section, concepts found within the chief complaint, history of present illness, and assessment and plan were the strongest predictors of relevance. This automated method of tracking can provide detailed, pertinent reports of clinical experience that does not require additional work from medical trainees. The coupling of section header identification and concept identification holds promise for other natural language processing tasks, such as clinical research or phenotype identification.
机译：研究生医学生必须表现出临床技能。当前的跟踪方法依靠人工或简单的电子输入来记录临床经验。我们评估了自动方法，从三名医学生的临床笔记（n = 290）中找到了10个机构定义的核心临床问题。每个注释都经过节标题识别算法和KnowledgeMap概念标识符的处理，以定位统一医学语言系统（UMLS）概念。表现最佳的自动搜索策略可对文件进行准确分类，其中包含对核心临床问题的主要讨论，接收者操作员特征曲线下的面积为0.90-0.94。 UMLS概念识别的召回率和精确度分别为0.91和0.92。在个人注释部分中，主要投诉中发现的概念，当前病史以及评估和计划是相关性的最强预测因子。这种自动跟踪方法可以提供详细，相关的临床经验报告，而无需医学培训人员的额外工作。段标题识别和概念识别的结合为其他自然语言处理任务（例如临床研究或表型识别）提供了希望。

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