Disambiguating biomedical acronyms using EMIM

Limsopatham, Nut; Santos, Rodrygo L. T.; Macdonald, Craig; Ounis, Iadh

doi:10.1145/2009916.2010125

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…One of the important research areas of searching in the medical domain is dealing with the complexity, ambiguity and inconsistency of the medical terminology [6,7,8]. For example, when referring to 'coronary heart disease', different medical practitioners may use terms, such as 'coronary artery disease', 'arteriosclerotic heart disease', 'CHD', or 'CAD'.…”

Section: Related Workmentioning

confidence: 99%

“…Within a patient search system, these EMRs could be exploited to identify cohorts for clinical studies [3,4,5]. One of the major challenges of searching in the medical domain is to deal with often complex, inconsistent and ambiguous terminology [6,7,8]. For example, it is commonly known by medical practitioners that 'cancer', 'carcinoma', 'CA', and 'malignant tumour' share a similar meaning.…”

Section: Introductionmentioning

confidence: 99%

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Inferring Conceptual Relationships When Ranking Patients

Limsopatham,

Macdonald,

Ounis

2017

Preprint

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Searching patients based on the relevance of their medical records is challenging because of the inherent implicit knowledge within the patients' medical records and queries. Such knowledge is known to the medical practitioners but may be hidden from a search system. For example, when searching for the patients with a heart disease, medical practitioners commonly know that patients who are taking the amiodarone medicine are relevant, since this drug is used to combat heart disease. In this article, we argue that leveraging such implicit knowledge improves the retrieval effectiveness, since it provides new evidence to infer the relevance of patients' medical records towards a query. Specifically, built upon existing conceptual representation for both medical records and queries, we proposed a novel expansion of queries that infers additional conceptual relationships from domain-specific resources as well as by extracting informative concepts from the top-ranked patients' medical records. We evaluate the retrieval effectiveness of our proposed approach in the context of the TREC 2011 and 2012 Medical Records track. Our results show the effectiveness of our approach to model the implicit knowledge in patient search, whereby the retrieval performance is significantly improved over both an effective conceptual representation baseline and an existing semantic query expansion baseline. In addition, we provide an analysis of the types of queries that the proposed approach is likely to be effective.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inferring Conceptual Relationships When Ranking Patients

Limsopatham,

Macdonald,

Ounis

2017

Preprint

Self Cite

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“…In order to solve disambiguation problem for the acronyms, there are researches that identified, clustered, and disambiguated acronyms by using C‐value, EMIM, context information, and Naïve Bayesian approaches. Although these studies obtained interesting results by using frequency‐based statistical methods, they failed to consider the semantics of co‐occurrence words (also known as context words).…”

Section: Related Workmentioning

confidence: 99%

Identifying the most appropriate expansion of acronyms used in wikipedia text

Choi

Kim

2014

Softw Pract Exp

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SUMMARYDuring the past several years, many studies have been conducted to determine the expansions (the expanded forms or definitions) of acronyms used in specific texts by using linguistic and syntactical approaches to overcome disambiguation problems. An acronym is an abbreviation formed of the initial components of multiple words. Using only these initial parts can result in huge mistakes when a machine conducts experiments to determine an acronym's meaning in given texts. Finding expansions of acronyms is currently not a major concern. However, identifying how a polysemous acronym is precisely being used in a specific text is difficult. To solve disambiguation problem acronyms have, this paper proposes a method to identify the most appropriate expansions of an acronym by analysing co-occurrence words found in articles on Wikipedia. Our goal is not to find an acronym's definition or expansion but to identify the most appropriate expansion of a given acronym as it is used in a given text. Through experiments, we demonstrate the proposed method's efficiency and performance by comparing several results.

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“…Medical terminology, which can be complex, inconsistent, and ambiguous, poses an important challenge when searching in the medical domain [9,10,12,15,16]. For examPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Learning to combine representations for medical records search

Limsopatham

Macdonald

Ounis

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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The complexity of medical terminology raises challenges when searching medical records. For example, 'cancer', 'tumour', and 'neoplasms', which are synonyms, may prevent a traditional search system from retrieving relevant records that contain only synonyms of the query terms. Prior works use bag-of-concepts approaches, to deal with this by representing medical terms sharing the same meanings using concepts from medical resources (e.g. MeSH). The relevance scores are then combined with a traditional bag-of-words representation, when inferring the relevance of medical records. Even though the existing approaches are effective, the predicted retrieval effectiveness of either the bag-of-words or bag-ofconcepts representation, which may be used to effectively model the score combination and hence improve retrieval performance, is not taken into account. In this paper, we propose a novel learning framework that models the importance of the bag-of-words and the bag-of-concepts representations, combining their scores on a per-query basis. Our proposed framework leverages retrieval performance predictors, such as the clarity score and AvIDF, calculated on both representations as learning features. We evaluate our proposed framework using the TREC Medical Records track's test collections. As our proposed framework can significantly outperform an existing approach that linearly merges the relevance scores, we conclude that retrieval performance predictors can be effectively leveraged when combining the relevance scores.

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Disambiguating biomedical acronyms using EMIM

Cited by 5 publications

References 4 publications

Inferring Conceptual Relationships When Ranking Patients

Inferring Conceptual Relationships When Ranking Patients

Identifying the most appropriate expansion of acronyms used in wikipedia text

Learning to combine representations for medical records search

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