Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.
Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.
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DOI：
10.15265/iy-2015-009
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发表时间：
2015-08-13
影响因子：
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通讯作者：
Dalianis, H
中科院分区：
文献类型：
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作者：
Velupillai, S;Mowery, D;Dalianis, H
目的：本文综述了临床自然语言处理（NLP）的最新进展，重点是语义分析和支持这种分析的关键子任务。方法：我们对2008年至2014年的临床NLP研究进行了文献综述，重点是最近的出版物（2012-2014），基于PubMed和ACL会议记录以及纳入论文的相关参考文献。该时间段内发表的重要文章均被纳入其中，并从语义分析的角度进行了讨论。确定了实现这种分析的三个关键临床NLP子任务：1）开发更有效的语料库创建方法（注释和去识别），2）生成用于提取含义的构建块（形态，句法和语义子任务），以及3）利用NLP进行临床实用性（NLP应用程序和临床用例基础设施）。最后，我们提供了一个最新的发展和未来的NLP开发和applications.CONCLUSIONS潜在领域的反思：有一个关键的NLP子任务，支持语义分析的进步增加。在许多情况下，NLP语义分析的性能接近人类之间的协议。用复杂语义信息模型标注的语料库的创建和发布极大地支持了新工具和方法的开发。对非英语语言的研究正在不断增长。NLP方法有时已成功应用于现实世界的临床任务。然而，在先进资源的开发与其在临床环境中的利用之间仍然存在差距。由于已建立的医疗保健计划以及通过广泛使用社交媒体和其他设备产生的额外患者来源，出现了大量新的临床用例。
OBJECTIVES: We present a review of recent advances in clinical Natural Language Processing (NLP), with a focus on semantic analysis and key subtasks that support such analysis.METHODS: We conducted a literature review of clinical NLP research from 2008 to 2014, emphasizing recent publications (2012-2014), based on PubMed and ACL proceedings as well as relevant referenced publications from the included papers.RESULTS: Significant articles published within this time-span were included and are discussed from the perspective of semantic analysis. Three key clinical NLP subtasks that enable such analysis were identified: 1) developing more efficient methods for corpus creation (annotation and de-identification), 2) generating building blocks for extracting meaning (morphological, syntactic, and semantic subtasks), and 3) leveraging NLP for clinical utility (NLP applications and infrastructure for clinical use cases). Finally, we provide a reflection upon most recent developments and potential areas of future NLP development and applications.CONCLUSIONS: There has been an increase of advances within key NLP subtasks that support semantic analysis. Performance of NLP semantic analysis is, in many cases, close to that of agreement between humans. The creation and release of corpora annotated with complex semantic information models has greatly supported the development of new tools and approaches. Research on non-English languages is continuously growing. NLP methods have sometimes been successfully employed in real-world clinical tasks. However, there is still a gap between the development of advanced resources and their utilization in clinical settings. A plethora of new clinical use cases are emerging due to established health care initiatives and additional patient-generated sources through the extensive use of social media and other devices.