Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV, or CHV for short), is a collection of medical terms written in plain English. It provides a list of simple, easy, and clear terms that laymen prefer to use rather than an equivalent professional medical term. The National Library of Medicine (NLM) has integrated and mapped the CHV terms to their Unified Medical Language System (UMLS). These CHV terms mapped to 56000 professional concepts on the UMLS. We found that about 48% of these laymen's terms are still jargon and matched with the professional terms on the UMLS. In this paper, we present an enhanced word embedding technique that generates new CHV terms from a consumer-generated text. We downloaded our corpus from a healthcare social media and evaluated our new method based on iterative feedback to word embedding using ground truth built from the existing CHV terms. Our feedback algorithm outperformed unmodified GLoVe and new CHV terms have been detected.
学习词汇术语的高维密集表示，也称为词嵌入，最近在自然语言处理和信息检索任务中引起了很多关注。嵌入向量通常基于大型语料库中的术语接近度来学习。这意味着在公知的词嵌入算法中的目标，word2vec是准确预测给定单词或上下文的相邻单词。然而，这个目标并不一定等同于许多信息检索（IR）任务的目标。各种IR任务的主要目标是捕获相关性，而不是术语接近度，句法，甚至语义相似性。这就是开发基于查询文档相关性信息学习单词表示的无监督的基于相关性的词嵌入模型的动机。在本文中，我们提出了两个学习模型，具有不同的目标函数，一个学习的词汇集为每个查询的相关性分布，和其他分类每个术语属于相关或不相关的类为每个查询。为了训练我们的模型，我们使用了超过600万个独特的查询和响应每个查询检索的排名最高的文档，这些文档被认为与查询相关。我们extraperitively评估我们的学习词表示模型使用两个IR任务：查询扩展和查询分类。在四个TREC集合上的查询扩展实验和在KDD Cup 2005数据集上的查询分类实验表明，基于相关性的词嵌入模型的性能显著优于最先进的基于邻近度的嵌入模型，如word2vec和GloVe。
Ranking medical jargon in electronic health record notes by adapted distant supervision
University of Massachusetts Medical School（马萨诸塞大学医学院）；University of Massachusetts（马萨诸塞大学）；Yale University（耶鲁大学）；Bedford Veterans Affairs Medical Center（贝德福德退伍军人事务医疗中心）
