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This paper proposes a novel feedback-based approach which considers the semantic association between a retrieved biomedical article and a pseudo feedback set.
In "A Feedback-based Approach to Utilizing Embeddings for Clinical Decision Support," authors propose a feedback-based approach which considers the semantic association between a retrieved biomedical article and a pseudo feedback set, hence improve the performance in biomedical articles retrieval.
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In this paper, we have proposed a novel feedback-based CDS method, which integrates the semantic similarity between a biomedical article and the corresponding pseudo-relevance feedback set into frequency-based models.
Results: Here, we report the results of a novel text-mining approach that extracts DNA sequences from biomedical articles and automatically maps them to genomic databases.
Those methods extract concepts from queries and biomedical articles, and further utilize concepts to apply query expansion or document ranking.
In this section, we introduce our proposed feedback-based CDS method, which considers the semantic similarity between a biomedical article to be scored and a pseudo feedback set.
With over 2 million biomedical articles published annually and a medical literature that has grown increasingly complex, physicians struggle to remain informed of the many new therapies and diagnostic tools that relate to their practices.
PubMed: http://www.ncbi.nlm.nih.gov/pubmed - The PubMed database contains more than 19 million citations for biomedical articles from a wide range of indexed journals and can be searched at no cost.
Instead, we estimate the semantic relevance of a biomedical article by measuring the semantic similarity between the article and a pseudo-relevance feedback set.
Similar idea is presented in [32], where the semantic similarity between the embeddings of the patient record and biomedical article is utilized to improve the CDS system.
Second, according to Table 5, our CDS method outperforms the method (BM25+Sim_{d_{Para}text Q}) proposed in [32], which integrates semantic evidence by measuring the cosine similarity between the embeddings of the patient record and biomedical article.
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