We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text. Seven literature databases were searched with a query combining the concepts of natural language processing and structured data capture. Two reviewers screened all records for relevance during two screening phases, and information about clinical NLP systems was collected from the final set of papers. A total of 7149 records (after removing duplicates) were retrieved and screened, and 86 were determined to fit the review criteria. These papers contained information about 71 different clinical NLP systems, which were then analyzed. The NLP systems address a wide variety of important clinical and research tasks. Certain tasks are well addressed by the existing systems, while others remain as open challenges that only a small number of systems attempt, such as extraction of temporal information or normalization of concepts to standard terminologies. This review has identified many NLP systems capable of processing clinical free text and generating structured output, and the information collected and evaluated here will be important for prioritizing development of new approaches for clinical NLP.

中文翻译：

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用于捕获和标准化非结构化临床信息的自然语言处理系统：系统综述。

我们遵循了基于系统评价和荟萃分析的“首选报告项目”的系统方法，以识别可从非结构化自由文本生成结构化信息的现有临床自然语言处理（NLP）系统。结合自然语言处理和结构化数据捕获的概念，使用查询查询了七个文献数据库。两名审稿人在两个筛选阶段筛选了所有记录的相关性，并且从最后一组论文中收集了有关临床NLP系统的信息。总共检索和筛选了7149条记录（除去重复项后），并确定有86条记录符合审查标准。这些论文包含有关71种不同临床NLP系统的信息，然后对其进行了分析。NLP系统可解决各种重要的临床和研究任务。现有系统很好地解决了某些任务，而其他任务则仍然是只有少数系统尝试进行的公开挑战，例如提取时间信息或将概念标准化为标准术语。这篇综述确定了许多能够处理临床免费文本并生成结构化输出的NLP系统，并且此处收集和评估的信息对于优先开发临床NLP的新方法至关重要。