Understanding and Using the Medical Subject Headings (MeSH) Vocabulary to Perform Literature Searches

Lowe, Henry J.

doi:10.1001/jama.1994.03510380059038

Cited by 344 publications

(155 citation statements)

References 16 publications

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“…If the term is associated with an image, that image appears on the right pane. The BW^image in the yellow box is a link to the corresponding Wikipedia entry National Library of Medicine's Medical Subject Headings (MeSH) [24,25].…”

Section: Discussionmentioning

confidence: 99%

PORTER: a Prototype System for Patient-Oriented Radiology Reporting

Cook

Kahn

2016

J Digit Imaging

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To empower patients to participate in their medical care and decision-making, effective communication is critical. In radiology, the clinical report is the primary medium of communication. Although radiologists historically have authored reports with the referring provider as the intended reader, patients increasingly access the reports through portals to electronic health record systems. We developed a system named PORTER (Patient-Oriented Radiology Reporter) to augment radiology reports with lay-language definitions. Our IRB-approved, HIPAA-compliant study protocol analyzed 100 knee MRI reports from an academic medical center to identify the most commonly utilized terms. A glossary of 313 terms was constructed to include definitions of the terms and, where available, links to reference sources and public-domain images. Flesch-Kincaid readability scores were computed to assure that definitions were readable at or below 10th-grade reading level. The system provided an interactive web site to view outpatient knee MRI exams. After logging in with their exam ID number and date of birth, patients viewed their report annotated with definitions from the glossary. Applicable images were displayed when the user's mouse hovered over a glossary term. This patient-oriented system can help empower patients to better understand their radiology results.

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Section: Discussionmentioning

confidence: 99%

PORTER: a Prototype System for Patient-Oriented Radiology Reporting

Cook

Kahn

2016

J Digit Imaging

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“…For each database, an individual search strategy was developed, using Medical Subject Headings (MeSH terms) as well as free text words [16,17,20]. Lists of subject headings unique to each database were used to generate part of the search strategies [21]. We evaluated the spectrum of terms used for FDG in the literature by reviewing a random sample of 100 oncological FDG-PET studies.…”

Section: Methodsmentioning

confidence: 99%

“…The search strategy was adapted for each database, since controlled-vocabulary search terms are not standardized between databases [4,21]. The yield of the strategy was evaluated for each database.…”

Section: Methodsmentioning

confidence: 99%

How to perform a comprehensive search for FDG-PET literature

et al. 2000

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In this study, a comprehensive, unbiassed search strategy for identifying literature on fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) in Medline, Embase and Current Contents was developed, with specific search strategies for each database, using MeSH terms as well as free text words for PET and FDG. To examine which text words apply to FDG, we evaluated the ways of spelling FDG in a random sample of FDG-PET articles (n = 100). These words were used as free text words in the two databases and overlap was determined. PET publications were identified using the text words "positron emission tomography" and "pet$" combined with the respective MeSH terms for each database. To compare the yield of the combined FDG-PET strategy in each database, the retrieved citations were downloaded to Pro-Cite 4.0. Finally, we added search terms for lung cancer, breast cancer, melanoma, head and neck cancer and lymphoma to our strategy and to a short strategy (consisting of the text words "positron emission tomography" and "fdg"). In order to measure the yield and precision (positive predictive value, PPV) of our search strategy and compare it with the short one, we screened the title and abstract of the retrieved citations. Reviewing a random sample of the FDG-PET literature yielded 56 different ways of spelling FDG. We confined the list to 11 text words, without missing articles. Of the publications retrieved by these text words, only 4% were indexed by the MeSH term "Fludeoxyglucose F18" in Medline and 29% by the MeSH-term "Fluorodeoxyglucose F18" in Embase. Only 51% of PET articles were indexed by the MeSH term "Tomography, emission-computed" in Medline and 40% by the MeSH term "Positron emission tomography" in Embase. The combined search strategy for identifying studies on FDG and PET resulted in 2865 publications in Medline and 2646 in Embase. Medline identified 1662 publications not found by Embase; Embase identified 1422 publications not found by Medline. Compared with the short strategy, our search strategy yielded on average 52% more publications (94%, 41% and 20% more in Medline, Embase and Current Contents, respectively). The PPV of our strategy (percent of publications that were really on PET, FDG and the specified subject) was 70%, compared with 76% using the short strategy. Regardless of the strategy used, Embase yielded more publications and was also slightly more specific than Medline. With the recommended strategy, FDG-PET publications can be identified more efficiently. We have shown the importance of searching more than one database and emphasize the use of both MeSH terms and text words in a search strategy. Standardization of the spelling of FDG and indexing of articles on FDG would substantially simplify searching.

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“…[12]; approved drug names from the Food and Drug Administration; genes were obtained from Locuslink [13], and ontological classifications for genes were obtained from the gene ontology consortium [9]. Acronyms for database terms were resolved within text, when encountered, using an acronym resolving general heuristic [22].…”

Section: Recognizing Terms Of Interest Within Medline Recordsmentioning

confidence: 99%

Using fuzzy set theory and scale-free network properties to relate MEDLINE terms

Wren

2005

Soft Comput

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Automated construction and annotation of biological networks is becoming increasingly important in bioinformatics as the amount of biological data increases. At the center of this are metrics required for relating biological entities such as genes, diseases, signaling molecules and chemical compounds. Co-occurrence of terms within abstracts is widely used to establish tentative relationships because it is easy to use, implement, understand, and is reasonably accurate. However, it is also very imprecise -the cutoffs for how many co-occurrences of terms are necessary to establish a relationship is arbitrary and the nature of the relationship is generic. Since the frequency of co-occurrence for terms usually follows a scale-free distribution, this property can be exploited to define degree of membership in fuzzy sets. Beginning with a set of co-occurrences for any biomedical term (or its synonyms), relations are defined by the overlap of sets, normalizing by the area under the curve that the two sets share. The ability of this method to rank the relative specificity of biological relationships is tested by comparing cumulative term co-occurrences within 7.5 million MEDLINE abstracts with focused summaries of gene function and disease association within LocusLink. On average, the fuzzy set ranking (FSR) was in the top 0.6% of all potential associations, showing a good correlation between domain overlap and the biological association between two terms.

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Understanding and Using the Medical Subject Headings (MeSH) Vocabulary to Perform Literature Searches

Cited by 344 publications

References 16 publications

PORTER: a Prototype System for Patient-Oriented Radiology Reporting

PORTER: a Prototype System for Patient-Oriented Radiology Reporting

How to perform a comprehensive search for FDG-PET literature

Using fuzzy set theory and scale-free network properties to relate MEDLINE terms

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