Expansion of medical vocabularies using distributional semantics on Japanese patient blogs

Abstract

BackgroundResearch on medical vocabulary expansion from large corpora has primarily been conducted using text written in English or similar languages, due to a limited availability of large biomedical corpora in most languages. Medical vocabularies are, however, essential also for text mining from corpora written in other languages than English and belonging to a variety of medical genres. The aim of this study was therefore to evaluate medical vocabulary expansion using a corpus very different from those previously used, in terms of grammar and orthographics, as well as in terms of text genre. This was carried out by applying a method based on distributional semantics to the task of extracting medical vocabulary terms from a large corpus of Japanese patient blogs.MethodsDistributional properties of terms were modelled with random indexing, followed by agglomerative hierarchical clustering of 3 ×100 seed terms from existing vocabularies, belonging to three semantic categories: Medical Finding, Pharmaceutical Drug and Body Part. By automatically extracting unknown terms close to the centroids of the created clusters, candidates for new terms to include in the vocabulary were suggested. The method was evaluated for its ability to retrieve the remaining n terms in existing medical vocabularies.ResultsRemoving case particles and using a context window size of 1+1 was a successful strategy for Medical Finding and Pharmaceutical Drug, while retaining case particles and using a window size of 8+8 was better for Body Part. For a 10n long candidate list, the use of different cluster sizes affected the result for Pharmaceutical Drug, while the effect was only marginal for the other two categories. For a list of top n candidates for Body Part, however, clusters with a size of up to two terms were slightly more useful than larger clusters. For Pharmaceutical Drug, the best settings resulted in a recall of 25 % for a candidate list of top n terms and a recall of 68 % for top 10n. For a candidate list of top 10n candidates, the second best results were obtained for Medical Finding: a recall of 58 %, compared to 46 % for Body Part. Only taking the top n candidates into account, however, resulted in a recall of 23 % for Body Part, compared to 16 % for Medical Finding.ConclusionsDifferent settings for corpus pre-processing, window sizes and cluster sizes were suitable for different semantic categories and for different lengths of candidate lists, showing the need to adapt parameters, not only to the language and text genre used, but also to the semantic category for which the vocabulary is to be expanded. The results show, however, that the investigated choices for pre-processing and parameter settings were successful, and that a Japanese blog corpus, which in many ways differs from those used in previous studies, can be a useful resource for medical vocabulary expansion.

Clinical Natural Language Processing in languages other than English: opportunities and challenges

“…Distributional semantics was used to create a semantic space of Japanese patient blogs, seed terms from the categories Medical Finding, Pharmaceutical Drug and Body Part were used to expand the vocabularies with promising results [ 49 ].…”

Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach

“…Our work is also related to previous studies that have used distributional semantics for lexicon expansion [ 35 - 37 ]. In this work, we used word embedding, one technique for distributional semantics, to generate one type of learning features for the ADS system to rank EHR terms.…”

“…Previous studies have used both unsupervised and supervised learning methods to prioritize terms for inclusion in biomedical and health knowledge resources [ 32 - 35 ]. Term recognition methods, which are widely used unsupervised methods for term extraction, use rules and statistics (eg, corpus-level word and term frequencies) to prioritize technical terms from domain-specific text corpora.…”

First Steps towards Building a Medical Lexicon for Spanish with Linguistic and Semantic Information

“…Approaches based on the Firthian Distributional hypothesis exploit distributional similarity metrics (Carroll et al, 2012). Among them, more recent distributional semantics methods represent terms in the vector space, or calculate wordembeddings to compute similarity measures between vectors, thus allowing the unsupervised expansion of domain terms (Pyysalo et al, 2013;Skeppstedt et al, 2013;Henriksson et al, 2014;Wang et al, 2015;Ahltorp et al, 2016;Segura-Bedmar and Martínez, 2017) or concept normalization (Limsopatham and Collier, 2016).…”