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An enhancement on Clinical Data Analytics Language (CliniDAL) by integration of free text concept search

机译：通过集成自由文本概念搜索增强了临床数据分析语言（CliniDAL）

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Much of the important patient information can only be found in patient narratives or in free text fields of structural schema of the Clinical Information System (CIS). So, the integration of free text search facilities will improve question answering on CISs. This paper describes a method for integrating free text search facility to the proposed Data Analytics Language (CliniDAL) to improve its capabilities at answering more common clinical questions. The proposed language constructs in CliniDAL's grammar enables its parser to recognize the part of the Restricted Natural Language Query (RNLQ) of the CliniDAL interface, which needs a free text resolution mechanism. Then the Natural Language Processing (NLP) approach of the CliniSearch tool finds the correct matches with the query. The search result is integrated into the translated CliniDAL query which can be executed to return a more comprehensive answer to the initial text query. 160 queries are tested in the current work to investigate the improvements on answering more common questions from a CIS, which result in a simple taxonomy of four query categories of: unanswerable queries, queries that require more evidence to be answered, queries requiring user interpretation and queries with suitable answers. Compatibility of query results between the structural schema and patient progress notes is examined which showed the usability of the approach in answering queries, confirming the results from different sources and finding any inconsistency in the stored data in the CIS. The proposed solution provides a simple mechanism for extracting knowledge from CISs.
机译：许多重要的患者信息只能在患者的叙述中或在临床信息系统（CIS）结构图的自由文本字段中找到。因此，免费文本搜索功能的集成将改善CIS上的问题解答。本文介绍了一种将自由文本搜索功能集成到拟议的数据分析语言（CliniDAL）中的方法，以提高其在回答更多常见临床问题时的功能。 CliniDAL语法中提出的语言构造使它的解析器能够识别CliniDAL接口的受限自然语言查询（RNLQ）的一部分，该部分需要一种自由文本解析机制。然后，CliniSearch工具的自然语言处理（NLP）方法会找到与查询正确的匹配项。搜索结果被集成到已翻译的CliniDAL查询中，可以执行该查询以返回对初始文本查询的更全面的答案。在当前的工作中测试了160个查询，以调查从CIS回答更常见问题的改进，从而得出以下四个查询类别的简单分类法：无法回答的查询，需要更多证据回答的查询，需要用户解释的查询和具有适当答案的查询。检查了结构方案和患者病历记录之间的查询结果的兼容性，这表明了该方法在回答查询，确认来自不同来源的结果以及在CIS中存储的数据中发现任何不一致之处的可用性。提出的解决方案提供了一种从CIS提取知识的简单机制。

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