Word Embedding for the French Natural Language in Health Care: Comparative Study

Dynomant, Emeric; Lelong, Romain; Dahamna, Badisse; Massonnaud, Clément; Kerdelhué, Gaëtan; Grosjean, Julien; Canu, Stéphane; Darmoni, Stéfan Jacques

doi:10.2196/12310

Cited by 21 publications

(28 citation statements)

References 20 publications

(20 reference statements)

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“…Dynomant et al [ 33 ] learned a variety of word embedding models on a corpus of French-language text documents originating from the Rouen University Hospital, in order to compare embedding methods. They used five distinct evaluation tasks resolving around the intrinsic properties of the embeddings.…”

Section: Resultsmentioning

confidence: 99%

Visualization of medical concepts represented using word embeddings: a scoping review

Oubenali

Messaoud²,

Filiot

et al. 2022

BMC Med Inform Decis Mak

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Background Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), to learn dense and low-dimensional word representations from large unlabeled corpora that capture the implicit semantics of words. Models like Word2Vec, GloVe or FastText have been broadly applied and reviewed in the bioinformatics and healthcare fields, most often to embed clinical notes or activity and diagnostic codes. Visualization of the learned embeddings has been used in a subset of these works, whether for exploratory or evaluation purposes. However, visualization practices tend to be heterogeneous, and lack overall guidelines. Objective This scoping review aims to describe the methods and strategies used to visualize medical concepts represented using word embedding methods. We aim to understand the objectives of the visualizations and their limits. Methods This scoping review summarizes different methods used to visualize word embeddings in healthcare. We followed the methodology proposed by Arksey and O’Malley (Int J Soc Res Methodol 8:19–32, 2005) and by Levac et al. (Implement Sci 5:69, 2010) to better analyze the data and provide a synthesis of the literature on the matter. Results We first obtained 471 unique articles from a search conducted in PubMed, MedRxiv and arXiv databases. 30 of these were effectively reviewed, based on our inclusion and exclusion criteria. 23 articles were excluded in the full review stage, resulting in the analysis of 7 papers that fully correspond to our inclusion criteria. Included papers pursued a variety of objectives and used distinct methods to evaluate their embeddings and to visualize them. Visualization also served heterogeneous purposes, being alternatively used as a way to explore the embeddings, to evaluate them or to merely illustrate properties otherwise formally assessed. Conclusions Visualization helps to explore embedding results (further dimensionality reduction, synthetic representation). However, it does not exhaust the information conveyed by the embeddings nor constitute a self-sustaining evaluation method of their pertinence.

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Section: Resultsmentioning

confidence: 99%

Visualization of medical concepts represented using word embeddings: a scoping review

Oubenali

Messaoud²,

Filiot

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…We have already developed a vectorial space trained on EDSaN and generated a hybrid semantic annotator [4,5], and document embeddings to create inter-scientific paper similarities in PubMed [6]. Moreover, our MeSH-gram neural network model extends word embedding vectors with MeSH concepts and improves semantic similarity and relatedness [7].…”

Section: Preliminary Resultsmentioning

confidence: 99%

Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis

Soualmia

Lafon

Darmoni

2021

Studies in Health Technology and Informatics

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In the context of the IA.TROMED project we intend to develop and evaluate original algorithmic methods that will rely on semantic enrichment of embeddings by combining new deep learning algorithms, such as models founded on transformers, and symbolic artificial intelligence. The documents’ embeddings, the graphs’ embeddings of biomedical concepts, and patients’ embeddings, all of them semantically enriched with aligned formal ontologies and semantic networks, will constitute a layer that will play the role of a queryable and searchable knowledge base that will supply the IA.TROMED’s clinical, predictive, and iatrogenic diagnosis support module.

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“…First, the translation process inevitably causes some data loss, even across Indo-European languages. In a French study, the UMNSRS word pair sets were translated into French, and only 73% of the similarity set and 71% of the relatedness set were translated and used [ 21 ]. In a comparable Spanish study, only 65% of the relatedness set and 67% of the similarity set were automatically rendered into Spanish because of regional differences in medical protocols and commercial drug names [ 22 ].…”

Section: Discussionmentioning

confidence: 99%

A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation

Yum¹,

Lee²,

Jang³

et al. 2021

JMIR Med Inform

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Background The fact that medical terms require special expertise and are becoming increasingly complex makes it difficult to employ natural language processing techniques in medical informatics. Several human-validated reference standards for medical terms have been developed to evaluate word embedding models using the semantic similarity and relatedness of medical word pairs. However, there are very few reference standards in non-English languages. In addition, because the existing reference standards were developed a long time ago, there is a need to develop an updated standard to represent recent findings in medical sciences. Objective We propose a new Korean word pair reference set to verify embedding models. Methods From January 2010 to December 2020, 518 medical textbooks, 72,844 health information news, and 15,698 medical research articles were collected, and the top 10,000 medical terms were selected to develop medical word pairs. Attending physicians (n=16) participated in the verification of the developed set with 607 word pairs. Results The proportion of word pairs answered by all participants was 90.8% (551/607) for the similarity task and 86.5% (525/605) for the relatedness task. The similarity and relatedness of the word pair showed a high correlation (ρ=0.70, P<.001). The intraclass correlation coefficients to assess the interrater agreements of the word pair sets were 0.47 on the similarity task and 0.53 on the relatedness task. The final reference standard was 604 word pairs for the similarity task and 599 word pairs for relatedness, excluding word pairs with answers corresponding to outliers and word pairs that were answered by less than 50% of all the respondents. When FastText models were applied to the final reference standard word pair sets, the embedding models learning medical documents had a higher correlation between the calculated cosine similarity scores compared to human-judged similarity and relatedness scores (namu, ρ=0.12 vs with medical text for the similarity task, ρ=0.47; namu, ρ=0.02 vs with medical text for the relatedness task, ρ=0.30). Conclusions Korean medical word pair reference standard sets for semantic similarity and relatedness were developed based on medical documents from the past 10 years. It is expected that our word pair reference sets will be actively utilized in the development of medical and multilingual natural language processing technology in the future.

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Word Embedding for the French Natural Language in Health Care: Comparative Study

Cited by 21 publications

References 20 publications

Visualization of medical concepts represented using word embeddings: a scoping review

Visualization of medical concepts represented using word embeddings: a scoping review

Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis

A Word Pair Dataset for Semantic Similarity and Relatedness in Korean Medical Vocabulary: Reference Development and Validation

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