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Expanding Consumer Health Vocabulary Across Languages Using Word Embeddings from User-Generated Content


Core Concepts
This research proposes a cross-lingual automatic term recognition framework to extend the English consumer health vocabulary (CHV) into other languages by leveraging word embeddings learned from comparable user-generated health content.
Abstract
The paper presents a cross-lingual automatic term recognition (ATR) framework to expand the English consumer health vocabulary (CHV) into other languages. The key steps are: Data Collection: The framework collects healthcare Q&A corpora in English and Chinese as the input. Pre-processing: It applies NLP techniques to clean the raw texts and extract medical entities from the corpora. Monolingual Word Vector Space Determination: The framework uses the skip-gram algorithm to determine the word vector spaces for each language independently. Space Alignment: It aligns the monolingual word spaces into a bilingual word vector space using a small set of medical entity translations as anchors. Word Expansion: Given a set of seed words, the framework retrieves their synonym candidates across languages using the bilingual word space and a dynamic threshold mechanism. The experimental results show that the bilingual word space induced by the proposed framework outperforms two state-of-the-art large language models (GPT-3.5-Turbo and Cohere Rerank) in identifying cross-lingual consumer health vocabulary. The framework only requires raw user-generated health corpora and a limited set of medical translations, reducing the human effort in compiling cross-lingual CHV.
Stats
The English healthcare Q&A corpus contains 520,659 documents with an average length of 754.11 characters. The Chinese healthcare Q&A corpus contains 259,709 documents with an average length of 177.70 characters.
Quotes
"The open-access and collaborative consumer health vocabulary (OAC CHV) is the controlled vocabulary for addressing such a challenge. Nevertheless, OAC CHV is only available in English, limiting its applicability to other languages." "Our framework only requires raw HCGC corpora and a limited size of medical translations, reducing human efforts in compiling cross-lingual CHV."

Deeper Inquiries

How can the proposed framework be extended to align the consumer health vocabulary across more than two languages?

The proposed framework can be extended to align consumer health vocabulary across more than two languages by incorporating additional monolingual word vector spaces and aligning them into a single cross-lingual word space. This extension would involve collecting HCGC corpora in multiple languages, determining word vector spaces for each language using word embedding techniques, and aligning these spaces into a unified cross-lingual word space. By leveraging a set of bilingual medical translation pairs as anchors in each language, the framework can align multiple word spaces simultaneously, enabling the identification of semantically similar words across multiple languages. This approach would facilitate the creation of a comprehensive cross-lingual consumer health vocabulary that spans across various languages.

What are the potential challenges in applying the learned cross-lingual consumer health vocabulary to real-world consumer-oriented healthcare applications?

There are several potential challenges in applying the learned cross-lingual consumer health vocabulary to real-world consumer-oriented healthcare applications. One challenge is ensuring the accuracy and relevance of the translated terms across languages, as nuances in language and cultural differences can impact the effectiveness of the vocabulary. Additionally, maintaining the consistency and updating the vocabulary to reflect evolving language trends and new medical terms can be a challenge. Another challenge is integrating the cross-lingual vocabulary into existing healthcare systems and applications, which may require significant technical adjustments and compatibility considerations. Furthermore, ensuring the privacy and security of health information when using the vocabulary in online platforms is crucial to maintain user trust and compliance with data protection regulations.

How can the framework leverage existing medical terminologies, such as MedDRA, to further improve the quality of the induced cross-lingual consumer health vocabulary?

The framework can leverage existing medical terminologies, such as MedDRA (Medical Dictionary for Regulatory Activities), to enhance the quality of the induced cross-lingual consumer health vocabulary in several ways. Firstly, by mapping the terms from the cross-lingual vocabulary to standardized concepts in MedDRA, the framework can ensure consistency and interoperability with established medical terminologies used in healthcare settings. This mapping can help align consumer-generated terms with professional medical terminology, improving the accuracy and precision of the vocabulary. Secondly, incorporating MedDRA codes or concepts into the cross-lingual vocabulary can enhance the semantic richness and specificity of the terms, enabling more precise information retrieval and analysis in consumer-oriented healthcare applications. Additionally, by leveraging the hierarchical structure and relationships within MedDRA, the framework can organize and categorize the consumer health vocabulary in a structured manner, facilitating better navigation and search capabilities for users seeking health information.
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