Learning Concept-Driven Document Embeddings for Medical Information Search

Abstract : Many medical tasks such as self-diagnosis, health-care assessment , and clinical trial patient recruitment involve the usage of information access tools. A key underlying step to achieve such tasks is the document-to-document matching which mostly fails to bridge the gap identified between raw level representations of information in documents and high-level human interpretation. In this paper, we study how to optimize the document representation by leveraging neural-based approaches to capture latent representations built upon both validated medical concepts specified in an external resource as well as the used words. We experimentally show the effectiveness of our proposed model used as a support of two different medical search tasks, namely health search and clinical search for cohorts.
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Gia-Hung Nguyen, Lynda Tamine, Laure Soulier, Nathalie Souf. Learning Concept-Driven Document Embeddings for Medical Information Search. Conference on Artificial Intelligence in Medicine (AIME 2017), Jun 2017, Vienna, Austria. ⟨hal-01517094⟩

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