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An adaptive term proximity based rocchio’s model for clinical decision support retrieval

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2019
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Title
An adaptive term proximity based rocchio’s model for clinical decision support retrieval
Published in
BMC Medical Informatics and Decision Making, December 2019
DOI 10.1186/s12911-019-0986-6
Pubmed ID
Authors

Min Pan, Yue Zhang, Qiang Zhu, Bo Sun, Tingting He, Xingpeng Jiang

Abstract

In order to better help doctors make decision in the clinical setting, research is necessary to connect electronic health record (EHR) with the biomedical literature. Pseudo Relevance Feedback (PRF) is a kind of classical query modification technique that has shown to be effective in many retrieval models and thus suitable for handling terse language and clinical jargons in EHR. Previous work has introduced a set of constraints (axioms) of traditional PRF model. However, in the feedback document, the importance degree of candidate term and the co-occurrence relationship between a candidate term and a query term. Most methods do not consider both of these factors. Intuitively, terms that have higher co-occurrence degree with a query term are more likely to be related to the query topic. In this paper, we incorporate original HAL model into the Rocchio's model, and propose a new concept of term proximity feedback weight. A HAL-based Rocchio's model in the query expansion, called HRoc, is proposed. Meanwhile, we design three normalization methods to better incorporate proximity information to query expansion. Finally, we introduce an adaptive parameter to replace the length of sliding window of HAL model, and it can select window size according to document length. Based on 2016 TREC Clinical Support medicine dataset, experimental results demonstrate that the proposed HRoc and HRoc_AP models superior to other advanced models, such as PRoc2 and TF-PRF methods on various evaluation metrics. Among them, compared with the Proc2 and TF-PRF models, the MAP of our model is increased by 8.5% and 12.24% respectively, while the F1 score of our model is increased by 7.86% and 9.88% respectively. The proposed HRoc model can effectively enhance the precision and the recall rate of Information Retrieval and gets a more precise result than other models. Furthermore, after introducing self-adaptive parameter, the advanced HRoc_AP model uses less hyper-parameters than other models while enjoys an equivalent performance, which greatly improves the efficiency and applicability of the model and thus helps clinicians to retrieve clinical support document effectively.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 11%
Researcher 2 11%
Librarian 1 6%
Student > Bachelor 1 6%
Other 1 6%
Other 2 11%
Unknown 9 50%
Readers by discipline Count As %
Unspecified 2 11%
Arts and Humanities 1 6%
Agricultural and Biological Sciences 1 6%
Medicine and Dentistry 1 6%
Engineering 1 6%
Other 0 0%
Unknown 12 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 December 2019.
All research outputs
#18,704,331
of 23,182,015 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,593
of 2,016 outputs
Outputs of similar age
#337,012
of 459,477 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#49
of 70 outputs
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