Expanding Consumer Health Vocabularies with Frequency-Conserving Internal Context Models
2018 IEEE International Conference on Healthcare Informatics (ICHI)(2018)
摘要
Consumer Health Vocabularies (CHVs) function as lexicons that help healthcare professionals and consumers communicate effectively regarding medical concepts. A CHV is a record of a list of terms that are used by consumers when discussing health-related issues, as well as the associated medical concepts and terminology. In this work, we describe an algorithm to identify candidate terms and associated concepts for inclusion in the CHV from analyzing user-generated text on internet health forums. The proposed algorithm aims to identify terms in user-generated text that are similar to existing terms in the CHV and identify the closest Universal Medical Language System (UMLS) concept for the candidate terms. The model utilizes internal contexts of phrases to generate a likelihood ranking for each phrase observed in the input data. We demonstrate a limited evaluation of model performance and present a list of candidate terms generated by the model.
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关键词
healthcare, vocabulary, vocabulary expansion, consumer health vocabulary
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