Learning to Refine Expansion Terms for Biomedical Information Retrieval Using Semantic Resources

Xu, Bo; Lin, Hongfei; Lin, Yuan

doi:10.1109/tcbb.2018.2801303

Cited by 15 publications

(16 citation statements)

References 28 publications

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“…We tuned the parameters of our method for 2006 collection with 2007 queries, and tuned the parameters for 2007 collection with 2006 queries. The selected parameters are reported in Table 3, which have been used in our previous work [26].…”

Section: Resultsmentioning

confidence: 99%

“…The candidate expansion terms should be highly correlated with the given query in terms of both relevance and diversity. We adopt a modified pseudo relevance feedback method [26] to extract the terms. The method has been proved to be effective in biomedical information retrieval, which considers term distribution in feedback documents and term distribution in Medical Subject Headings (MeSH) to extract useful expansion terms for further refinement.…”

Section: Methodsmentioning

confidence: 99%

“…Inspired by the related work, we propose a supervised query expansion method for diversity-oriented biomedical information retrieval. The proposed method modify our learn-to-rank based query expansion framework [26] from two respects. One is assigning diversity-oriented term labels and extract different types of resource-based term features.…”

Section: Related Workmentioning

confidence: 99%

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A supervised term ranking model for diversity enhanced biomedical information retrieval

Lin

Yang

et al. 2019

BMC Bioinformatics

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BackgroundThe number of biomedical research articles have increased exponentially with the advancement of biomedicine in recent years. These articles have thus brought a great difficulty in obtaining the needed information of researchers. Information retrieval technologies seek to tackle the problem. However, information needs cannot be completely satisfied by directly introducing the existing information retrieval techniques. Therefore, biomedical information retrieval not only focuses on the relevance of search results, but also aims to promote the completeness of the results, which is referred as the diversity-oriented retrieval.ResultsWe address the diversity-oriented biomedical retrieval task using a supervised term ranking model. The model is learned through a supervised query expansion process for term refinement. Based on the model, the most relevant and diversified terms are selected to enrich the original query. The expanded query is then fed into a second retrieval to improve the relevance and diversity of search results. To this end, we propose three diversity-oriented optimization strategies in our model, including the diversified term labeling strategy, the biomedical resource-based term features and a diversity-oriented group sampling learning method. Experimental results on TREC Genomics collections demonstrate the effectiveness of the proposed model in improving the relevance and the diversity of search results.ConclusionsThe proposed three strategies jointly contribute to the improvement of biomedical retrieval performance. Our model yields more relevant and diversified results than the state-of-the-art baseline models. Moreover, our method provides a general framework for improving biomedical retrieval performance, and can be used as the basis for future work.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A supervised term ranking model for diversity enhanced biomedical information retrieval

Lin

Yang

et al. 2019

BMC Bioinformatics

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“…Therefore, apart from generating expansion terms arbitrarily, a lot of studies show that the quality of added terms and weights assigned to these expansion terms could largely affect the result of retrieval. In the medical IR area, Xu et al used PRF to generate expansion terms which are mapped into MeSH, and refined the candidate expansion terms by training term-ranking models to select the most relevant ones for query expansion [12].…”

Section: Query Expansionmentioning

confidence: 99%

A Medical Literature Search System for Identifying Effective Treatments in Precision Medicine

Qu,

Wang

2019

Preprint

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The Precision Medicine Initiative states that treatments for a patient should take into account not only the patient's disease, but his/her specific genetic variation as well. The vast biomedical literature holds the potential for physicians to identify effective treatment options for a cancer patient. However, the complexity and ambiguity of medical terms can result in vocabulary mismatch between the physician's query and the literature. The physician's search intent (finding treatments instead of other types of studies) is difficult to explicitly formulate in a query. Therefore, simple ad hoc retrieval approach will suffer from low recall and precision. In this paper, we propose a new retrieval system that helps physicians identify effective treatments in precision medicine. Given a cancer patient with a specific disease, genetic variation, and demographic information, the system aims to identify biomedical publications that report effective treatments. We approach this goal from two directions. First, we expand the original disease and gene terms using biomedical knowledge bases to improve recall of the initial retrieval. We then improve precision by promoting treatment-related publications to the top using a machine learning reranker trained on 2017 Text Retrieval Conference Precision Medicine (PM) track corpus. Batch evaluation results on 2018 PM track corpus show that the proposed approach effectively improves both recall and precision, achieving performance comparable to the top entries on the leaderboard of 2018 PM track.

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“…Inevitably, CDS search based on PRF also suffers from a well-known problem of PRF called topic drifting, which degrades retrieval performance as the intention of the query topic could change in an unexpected direction due to erroneous extraction of the concepts to be expanded [34]. In order to reduce the effect of query drift, word embedding approach was utilized for term expansion [35] and search diversification [36]. Meanwhile in this paper, at the passage-level expansion, words that co-occur with the initial query are detected as additional medical concepts; and at the document-level, relevance of the concepts can be verified by performing PRF again on document corpus that has more coverage over the medical field than the passage corpus.…”

Section: Related Workmentioning

confidence: 99%

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

Han

Shim

2018

Journal of Healthcare Engineering

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Clinical decision support (CDS) search is performed to retrieve key medical literature that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case in hand. In this paper, we present a novel CDS search framework designed for passage retrieval from biomedical textbooks in order to support clinical decision-making using laboratory test results. The framework utilizes two unique characteristics of the textual reports derived from the test results, which are syntax variation and negation information. The proposed framework consists of three components: domain ontology, index repository, and query processing engine. We first created a domain ontology to resolve syntax variation by applying the ontology to detect medical concepts from the test results with language translation. We then preprocessed and performed indexing of biomedical textbooks recommended by clinicians for passage retrieval. We finally built the query-processing engine tailored for CDS, including translation, concept detection, query expansion, pseudo-relevance feedback at the local and global levels, and ranking with differential weighting of negation information. To evaluate the effectiveness of the proposed framework, we followed the standard information retrieval evaluation procedure. An evaluation dataset was created, including 28,581 textual reports for 30 laboratory test results and 56,228 passages from widely used biomedical textbooks, recommended by clinicians. Overall, our proposed passage retrieval framework, GPRF-NEG, outperforms the baseline by 36.2, 100.5, and 69.7 percent for MRR, R-precision, and Precision at 5, respectively. Our study results indicate that the proposed CDS search framework specifically designed for passage retrieval of biomedical literature represents a practically viable choice for clinicians as it supports their decision-making processes by providing relevant passages extracted from the sources that they prefer to refer to, with improved performances.

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Learning to Refine Expansion Terms for Biomedical Information Retrieval Using Semantic Resources

Cited by 15 publications

References 28 publications

A supervised term ranking model for diversity enhanced biomedical information retrieval

A supervised term ranking model for diversity enhanced biomedical information retrieval

A Medical Literature Search System for Identifying Effective Treatments in Precision Medicine

A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting

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