Segura-Bedmar Isabel, Martínez Paloma
Computer Science Departament, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, Madrid, Spain.
J Biomed Semantics. 2017 Sep 29;8(1):45. doi: 10.1186/s13326-017-0156-7.
Drug Package Leaflets (DPLs) provide information for patients on how to safely use medicines. Pharmaceutical companies are responsible for producing these documents. However, several studies have shown that patients usually have problems in understanding sections describing posology (dosage quantity and prescription), contraindications and adverse drug reactions. An ultimate goal of this work is to provide an automatic approach that helps these companies to write drug package leaflets in an easy-to-understand language. Natural language processing has become a powerful tool for improving patient care and advancing medicine because it leads to automatically process the large amount of unstructured information needed for patient care. However, to the best of our knowledge, no research has been done on the automatic simplification of drug package leaflets. In a previous work, we proposed to use domain terminological resources for gathering a set of synonyms for a given target term. A potential drawback of this approach is that it depends heavily on the existence of dictionaries, however these are not always available for any domain and language or if they exist, their coverage is very scarce. To overcome this limitation, we propose the use of word embeddings to identify the simplest synonym for a given term. Word embedding models represent each word in a corpus with a vector in a semantic space. Our approach is based on assumption that synonyms should have close vectors because they occur in similar contexts.
In our evaluation, we used the corpus EasyDPL (Easy Drug Package Leaflets), a collection of 306 leaflets written in Spanish and manually annotated with 1400 adverse drug effects and their simplest synonyms. We focus on leaflets written in Spanish because it is the second most widely spoken language on the world, but as for the existence of terminological resources, the Spanish language is usually less prolific than the English language. Our experiments show an accuracy of 38.5% using word embeddings.
This work provides a promising approach to simplify DPLs without using terminological resources or parallel corpora. Moreover, it could be easily adapted to different domains and languages. However, more research efforts are needed to improve our approach based on word embedding because it does not overcome our previous work using dictionaries yet.
药品说明书为患者提供有关如何安全用药的信息。制药公司负责编写这些文件。然而，多项研究表明，患者在理解说明书中描述剂量（剂量数量和处方）、禁忌和药物不良反应的部分时通常会遇到问题。这项工作的最终目标是提供一种自动方法，帮助这些公司以通俗易懂的语言编写药品说明书。自然语言处理已成为改善患者护理和推动医学发展的强大工具，因为它能够自动处理患者护理所需的大量非结构化信息。然而，据我们所知，尚未有人对药品说明书的自动简化进行研究。在之前的一项工作中，我们提议使用领域术语资源为给定的目标术语收集一组同义词。这种方法的一个潜在缺点是它严重依赖词典的存在，然而并非所有领域和语言都有词典，或者即使有，其覆盖范围也非常有限。为克服这一限制，我们提议使用词嵌入来识别给定术语的最简单同义词。词嵌入模型用语义空间中的向量表示语料库中的每个单词。我们的方法基于这样的假设：同义词应该具有相近的向量，因为它们出现在相似的语境中。
在我们的评估中，我们使用了语料库EasyDPL（简易药品说明书），这是一组306份用西班牙语编写的说明书，并手动标注了1,400种药物不良反应及其最简单的同义词。我们专注于用西班牙语编写的说明书，因为它是世界上使用第二广泛的语言，但就术语资源的存在而言，西班牙语通常不如英语丰富。我们的实验表明，使用词嵌入的准确率为38.5%。
这项工作提供了一种有前景的方法来简化药品说明书，而无需使用术语资源或平行语料库。此外，它可以很容易地适应不同的领域和语言。然而，需要更多的研究工作来改进我们基于词嵌入的方法，因为它尚未超越我们之前使用词典的工作。
