Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA.
J Am Med Inform Assoc. 2018 Jun 1;25(6):679-685. doi: 10.1093/jamia/ocx152.
Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus.
We apply word2vec, a distributional semantics software package, to the text of radiology notes to identify synonyms for RadLex, a structured lexicon of radiology terms. We stratify performance by term category, term frequency, number of tokens in the term, vector magnitude, and the context window used in vector building.
Ranking candidates based on distributional similarity to a target term results in high curation efficiency: on a ranked list of 775 249 terms, >50% of synonyms occurred within the first 25 terms. Synonyms are easier to find if the target term is a phrase rather than a single word, if it occurs at least 100× in the corpus, and if its vector magnitude is between 4 and 5. Some RadLex categories, such as anatomical substances, are easier to identify synonyms for than others.
The unstructured text of clinical notes contains a wealth of information about human diseases and treatment patterns. However, searching and retrieving information from clinical notes often suffer due to variations in how similar concepts are described in the text. Biomedical lexicons address this challenge, but are expensive to produce and maintain. Distributional semantics algorithms can assist lexicon curation, saving researchers time and money.
分布语义算法通过从大型语料库中学习单词和短语的向量空间表示，根据上下文使用模式识别相关术语。我们假设分布语义可以通过从语料库中挖掘同义词来加速临床领域（放射学）的词汇扩展。
我们将 word2vec（一种分布语义软件包）应用于放射学笔记的文本中，以识别 RadLex（放射学术语的结构化词汇）的同义词。我们根据术语类别、术语频率、术语中的标记数量、向量幅度以及用于构建向量的上下文窗口对性能进行分层。
根据与目标术语的分布相似性对候选术语进行排序会产生很高的编校效率：在 775249 个术语的排名列表中，超过 50%的同义词出现在前 25 个术语中。如果目标术语是短语而不是单个单词，如果它在语料库中至少出现 100 次，并且其向量幅度在 4 到 5 之间，则更容易找到同义词。一些 RadLex 类别，如解剖物质，比其他类别更容易识别同义词。
临床笔记的非结构化文本包含有关人类疾病和治疗模式的大量信息。然而，由于文本中描述相似概念的方式存在差异，因此从临床笔记中搜索和检索信息往往会遇到困难。生物医学词汇表解决了这一挑战，但制作和维护成本很高。分布语义算法可以辅助词汇编校，为研究人员节省时间和金钱。
