An intelligent search engine and GUI-based efficient MEDLINE search tool based on deep syntactic parsing

Ohta, Tomoko; Miyao, Yusuke; Ninomiya, Takashi; Tsuruoka, Yoshimasa; Yakushiji, Akane; Masuda, Katsuya; Takeuchi, Jumpei; Yoshida, Kazuhiro; Hara, Tatsunori; Kim, Jin-Dong; Tateisi, Yuka; Tsujii, Jun’ichi

doi:10.3115/1225403.1225408

Cited by 25 publications

(17 citation statements)

References 12 publications

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“…Nevertheless, we intend to train the model using a larger number of more diverse gold-standard two-node searches that cover a wider range of topics, literature size and literature coherence. It is also likely that incorporating additional features related to conceptual language processing (Cohen and Hersh, 2005) may improve overall performance further: for example, it may be worthwhile to give differential weight to B-terms that are two or three word phrases (Nakagawa and Mori, 1998), noun phrases (Bennett et al, 1999), abbreviations (Zhou et al, 2006), that correspond to standard biomedical terminology (Bodenreider, 2004), or that correspond to particular linguistic functions such as the subject or object of a sentence (Ohta et al, 2006). As well, B-terms that refer to the same thing (spelling variants, synonyms or abbreviations and their corresponding long forms) should ideally be merged and considered together.…”

Section: Discussionmentioning

confidence: 99%

A quantitative model for linking two disparate sets of articles in MEDLINE

Torvik

Smalheiser

2007

Bioinformatics

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Background: Identifying information that implicitly links two disparate sets of articles is a fundamental and intuitive data mining strategy that can help investigators address real scientific questions. The Arrowsmith two-node search finds title words and phrases (so-called B-terms) that are shared across two sets of articles within MEDLINE and displays them in a manner that facilitates human assessment. A serious stumbling-block has been the lack of a quantitative model for predicting which of the hundreds if not thousands of B-terms computed for a given search are most likely to be relevant to the investigator. Methodology/Principal Findings: Using a public two-node search interface, field testers devised a set of two-node searches under real life conditions and a certain number of B-terms were marked relevant. These were employed as 'gold standards;' each B-term was characterized according to eight complementary features that were strongly correlated with relevance. A logistic regression model was developed that permits one to estimate the probability of relevance for each B-term, to rank B-terms according to their likely relevance, and to estimate the overall number of relevant B-terms inherent in a given two-node search. Conclusions/Significance: The model greatly simplifies and streamlines the process of carrying out a two-node search, and may be applicable to a number of other literature-based discovery applications, including the so-called one-node search and related gene-centric strategies that incorporate implicit links to predict how genes may be related to each other and to human diseases. This should encourage much wider exploration of text mining for implicit information among the general scientific community. Availability: Two-node searches can be carried out freely at http:// arrowsmith

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Section: Discussionmentioning

confidence: 99%

A quantitative model for linking two disparate sets of articles in MEDLINE

Torvik

Smalheiser

2007

Bioinformatics

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“…This work contributes to precise interpretation of biomedical texts for purposes of search (1, 3, 27), research (4) and data mining (2, 28). Cognition Search has features in common with other NLP software (6, 8, 29), but unique to Cognition Search are its very large hand built lexical resources with synonymy, ontology, built with all linguistic features encoded, and the linguistically-based morphology, sense disambiguation and parsing that draw upon these lexical resources. The present work relied upon the existing hand-built lexical resources, bootstrapping them for semi-automated lexical acquisition in the biomedical domain.…”

Section: Discussionmentioning

confidence: 99%

“…Google Scholar (http://scholar.google.com/), Highwire press (http://highwire.stanford.edu/lists/freeart.dtl) whereas other relatively commercial sources of this information is present at Scopus (http://www.scopus.com/scopus/home.url), Ovid (http://www.ovid.com/site/index.jsp), and Infotrieve (http://www4.infotrieve.com/newMEDLINE/search.asp). Many of these technologies use aspects of linguistic processing (4, 6–15) such as synonymy and ontology. These features improve recall.…”

Section: Introductionmentioning

confidence: 99%

Natural Language Query in the Biochemistry and Molecular Biology Domains Based on Cognition Search™

et al. 2008

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Motivation:With the increasing volume of scientific papers and heterogeneous nomenclature in the biomedical literature, it is apparent that an improvement over standard pattern matching available in existing search engines is required. Cognition Search Information Retrieval (CSIR) is a natural language processing (NLP) technology that possesses a large dictionary (lexicon) and large semantic databases, such that search can be based on meaning. Encoded synonymy, ontological relationships, phrases, and seeds for word sense disambiguation offer significant improvement over pattern matching. Thus, the CSIR has the right architecture to form the basis for a scientific search engine.Result:Here we have augmented CSIR to improve access to the MEDLINE database of scientific abstracts. New biochemical, molecular biological and medical language and acronyms were introduced from curated web-based sources. The resulting system was used to interpret MEDLINE abstracts. Meaning-based search of MEDLINE abstracts yields high precision (estimated at >90%), and high recall (estimated at >90%), where synonym, ontology, phrases and sense seeds have been encoded. The present implementation can be found at http://MEDLINE.cognition.com.Contact:Elizabeth.goldsmith@UTsouthwestern.eduKathleen.dahlgren@cognition.com

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“…Subtypes of medical interventions correspond to various methods for patient treatment and prophylaxis. Examples of medical intervention subtypes were taken from the Clinicaltrials.gov 3 Internet resource: 1). Drug; 2).…”

Section: Problem Statementmentioning

confidence: 99%

The data retrieval optimization from the perspective of evidence-based medicine

Dobrynin¹,

Balykina²,

Kamalov³

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-The paper is devoted to classification of MEDLINE abstracts into categories that correspond to types of medical interventions -types of patient treatments. This set of categories was extracted from Clinicaltrials.gov web site. Few classification algorithms were tested including Multinomial Naive Bayes, Multinomial Logistic Regression, and Linear SVM implementations from sklearn machine learning library. Document marking was based on the consideration of abstracts containing links to the Clinicaltrials.gov Web site. As the result of an automatical marking 3534 abstracts were marked for training and testing the set of algorithms metioned above. Best result of multinomial classification was achieved by Linear SVM with macro evaluation precision 70.06%, recall 55.62% and F-measure 62.01%, and micro evaluation precision 64.91%, recall 79.13% and F-measure 71.32%.

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An intelligent search engine and GUI-based efficient MEDLINE search tool based on deep syntactic parsing

Cited by 25 publications

References 12 publications

A quantitative model for linking two disparate sets of articles in MEDLINE

A quantitative model for linking two disparate sets of articles in MEDLINE

Natural Language Query in the Biochemistry and Molecular Biology Domains Based on Cognition Search™

The data retrieval optimization from the perspective of evidence-based medicine

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