Knowledge Author: facilitating user-driven, domain content development to support clinical information extraction.
Knowledge Author: facilitating user-driven, domain content development to support clinical information extraction.
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DOI：
10.1186/s13326-016-0086-9
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发表时间：
2016-06-23
影响因子：
1.9
通讯作者：
Chapman WW
中科院分区：
文献类型：
--
作者：
Scuba W;Tharp M;Mowery D;Tseytlin E;Liu Y;Drews FA;Chapman WW
临床自然语言处理（NLP）系统需要一个由特定领域概念、其词汇变体和相关修饰语组成的语义模式，以准确地从临床文本中提取信息。NLP系统利用这种模式来构建概念并从自由文本中提取含义。在临床领域中，创建语义模式通常需要来自领域专家（诸如临床医生）和NLP专家两者的输入，NLP专家将从临床医生的领域专业知识创建的临床概念表示为可由NLP系统使用的可计算格式。这项工作的目标是开发一个基于网络的工具，知识作者，桥梁之间的差距差距的临床领域专家和自然语言处理系统的发展，促进发展的领域内容表示的语义模式，从临床自由文本中提取信息。Knowledge Author是一个基于Web的推荐系统，支持用户开发临床NLP应用所需的领域内容。Knowledge Author的示意图模型利用了一组从Secondary Use Clinical Element Models和Common Type System派生的语义类型，以允许用户快速创建和修改领域相关概念。诸如协作开发和通过将概念映射到统一医学语言系统元词库数据库来提供领域内容建议等功能进一步支持领域内容创建过程。进行了两项概念验证研究，以评估系统的性能。第一项研究评估了知识作者创建广泛概念的灵活性。创建了115个概念的数据集，其中87个（76%）能够使用知识作者创建。第二项研究通过使用知识作者和NLP系统pyConText从34份临床自由文本放射学报告中提取代表临床元素颈动脉狭窄的概念和相关修饰符，评估了知识作者在NLP系统中输出的有效性。知识作者的领域内容产生了对概念的高回忆（目标发现：86%）和对修饰语的不同回忆（确定性：91%，片面性：80%，神经血管解剖学：46%）。Knowledge Author可以通过支持领域专家创建语义模式来支持临床领域内容开发以进行信息提取。本文的在线版本（doi：10.1186/s13326-016-0086-9）包含补充材料，可供授权用户使用。
Clinical Natural Language Processing (NLP) systems require a semantic schema comprised of domain-specific concepts, their lexical variants, and associated modifiers to accurately extract information from clinical texts. An NLP system leverages this schema to structure concepts and extract meaning from the free texts. In the clinical domain, creating a semantic schema typically requires input from both a domain expert, such as a clinician, and an NLP expert who will represent clinical concepts created from the clinician’s domain expertise into a computable format usable by an NLP system. The goal of this work is to develop a web-based tool, Knowledge Author, that bridges the gap between the clinical domain expert and the NLP system development by facilitating the development of domain content represented in a semantic schema for extracting information from clinical free-text. Knowledge Author is a web-based, recommendation system that supports users in developing domain content necessary for clinical NLP applications. Knowledge Author’s schematic model leverages a set of semantic types derived from the Secondary Use Clinical Element Models and the Common Type System to allow the user to quickly create and modify domain-related concepts. Features such as collaborative development and providing domain content suggestions through the mapping of concepts to the Unified Medical Language System Metathesaurus database further supports the domain content creation process. Two proof of concept studies were performed to evaluate the system’s performance. The first study evaluated Knowledge Author’s flexibility to create a broad range of concepts. A dataset of 115 concepts was created of which 87 (76 %) were able to be created using Knowledge Author. The second study evaluated the effectiveness of Knowledge Author’s output in an NLP system by extracting concepts and associated modifiers representing a clinical element, carotid stenosis, from 34 clinical free-text radiology reports using Knowledge Author and an NLP system, pyConText. Knowledge Author’s domain content produced high recall for concepts (targeted findings: 86 %) and varied recall for modifiers (certainty: 91 % sidedness: 80 %, neurovascular anatomy: 46 %). Knowledge Author can support clinical domain content development for information extraction by supporting semantic schema creation by domain experts. The online version of this article (doi:10.1186/s13326-016-0086-9) contains supplementary material, which is available to authorized users.