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Exact(9)
6a instead is the appropriate query sentence.
Medical terms are only a part of the original query sentence.
Whether UMLS-E or UMLS-W, the MAP value obtained using only medical term (α = 0) to query is higher than the MAP value obtained using only the original query sentence (α = 1).
Therefore, we propose the algorithm to add the medical-related information to the weight adjustment and more emphasis on the weight adjustment of the medical terms of the query sentence in this paper.
Intuitively, the term analyzer transforms the query sentence using terms in the schema graph concepts.
With the PTQL query, sentence S1 matches the criteria of the query and the triplet <ciprofloxacin, increases, clozapine> is formed.
Similar(51)
Therefore, we combine the original query sentences Qorigin with medical terms to construct new queries.
The k-means clustering algorithm was employed to cluster the query sentences.
The query sentences in the electronic medical record often contain some medical terms as a limiting condition.
More specifically, authorization policies are expressed in an SQL-like language which can be easily rewritten into query sentences for execution.
Finally, we proposed a method of using self-information to measure medical terms, combined with original query sentences to reconstruct queries, and improved the performance of electronic medical record retrieval system.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com