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Learning to reformulate long queries for clinical decision support
Author(s) -
Soldaini Luca,
Yates Andrew,
Goharian Nazli
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23924
Subject(s) - computer science , learning to rank , relevance (law) , field (mathematics) , filter (signal processing) , component (thermodynamics) , key (lock) , information retrieval , domain (mathematical analysis) , deep learning , data science , rank (graph theory) , data mining , artificial intelligence , machine learning , ranking (information retrieval) , mathematical analysis , physics , mathematics , computer security , combinatorics , political science , pure mathematics , law , computer vision , thermodynamics
The large volume of biomedical literature poses a serious problem for medical professionals, who are often struggling to keep current with it. At the same time, many health providers consider knowledge of the latest literature in their field a key component for successful clinical practice. In this work, we introduce two systems designed to help retrieving medical literature. Both receive a long, discursive clinical note as input query, and return highly relevant literature that could be used in support of clinical practice. The first system is an improved version of a method previously proposed by the authors; it combines pseudo relevance feedback and a domain‐specific term filter to reformulate the query. The second is an approach that uses a deep neural network to reformulate a clinical note. Both approaches were evaluated on the 2014 and 2015 TREC CDS datasets; in our tests, they outperform the previously proposed method by up to 28% in inferred NDCG; furthermore, they are competitive with the state of the art, achieving up to 8% improvement in inferred NDCG.