A knowledge-driven approach to biomedical document conceptualization

Zheng, Haitao; Borchert, Charles; Jiang, Yong

doi:10.1016/j.artmed.2010.02.005

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…For example Zheng et al successfully used MeSH headings to build a concept--document matrix to facilitate biomedical document search. 8 Significant improvements using concept--based IR are achieved in genomic information retrieval. Zhou et al 9 developed a concept matching algorithm that utilised both the UMLS ontology and MeSH headings; their system significantly outperformed keyword--based systems.…”

Section: Concept--based Irmentioning

confidence: 99%

Towards Semantic Search and Inference in Electronic Medical Records: an approach using concept based information retreival

Koopman

Bruza

Sitbon

et al. 2012

AMJ

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Section: Concept--based Irmentioning

confidence: 99%

Towards Semantic Search and Inference in Electronic Medical Records: an approach using concept based information retreival

Koopman

Bruza

Sitbon

et al. 2012

AMJ

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“…SecondString [17], an open-source Java toolkit, was used as an experimental test bed. During the experiments, each term was matched against those within a set of candidate pairs.…”

Section: Resultsmentioning

confidence: 99%

“…Zhen et al [17] introduced a TFIDF string distance method within their clustering algorithm and applied it to biomedical ontologies. The evaluation of their method demonstrated superior values of the F -measure on two datasets derived from the MeSH and GO ontologies.…”

Section: Introductionmentioning

confidence: 99%

Mapping biological entities using the longest approximately common prefix method

2014

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BackgroundThe significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction. The task of source integration in the Unified Medical Language System (UMLS) requires considerable expert effort despite the presence of various computational tools. This problem warrants the search for a new method for approximate string matching and its UMLS-based evaluation.ResultsThis paper introduces the Longest Approximately Common Prefix (LACP) method as an algorithm for approximate string matching that runs in linear time. We compare the LACP method for performance, precision and speed to nine other well-known string matching algorithms. As test data, we use two multiple-source samples from the Unified Medical Language System (UMLS) and two SNOMED Clinical Terms-based samples. In addition, we present a spell checker based on the LACP method.ConclusionsThe Longest Approximately Common Prefix method completes its string similarity evaluations in less time than all nine string similarity methods used for comparison. The Longest Approximately Common Prefix outperforms these nine approximate string matching methods in its Maximum F1 measure when evaluated on three out of the four datasets, and in its average precision on two of the four datasets.

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“…Let threshold p be the value that determines what type of the k-rank approximation should be retained in matrix A. Consequently, we define k as the minimum value that satisfies the formula (4) [50,51].…”

Section: Process Of Clustering Based On Fuzzy Analysismentioning

confidence: 99%