MEDLINE

According to the U.S. National Library of Medicine, "MEDLINE® (Medical Literature Analysis and Retrieval System Online) is the U.S. National Library of Medicine's® (NLM) premier bibliographic database that contains over 16 million references to journal articles in life sciences with a concentration on biomedicine. A distinctive feature of MEDLINE is that the records are indexed with NLM's Medical Subject Headings (MeSH®)."

PubMed is the National Library of Medicine's free online search system for MEDLINE.

Structure
MEDLINE® (Medical Literature Analysis and Retrieval System Online) is a database of predominantly biomedical bibliographic citations maintained by the U.S. National Library of Medicine (NLM). The process sofr selecting journals is described. Each citation includes bibliographic data, abstract if available, links to full text of the article and keywords. The keywords are indexed with the NLM's Medical Subject Headings (MeSH®) and subheadings.

The important MeSH terms “Randomized Controlled Trial” and “Clinical Controlled Trial” were introduced in 1991 and 1995, respectively. The Cochrane Collaboration helps MEDLINE correctly retag articles with these terms.

The National Library of Medicine's Indexing Initiative is trying to automate assignment of MeSH terms.

The National Library of Medicine is investigated whether indexing MeSH terms can be either fully or semi-automated.

Methods to improve searching MEDLINE
There is much ongoing research into improving MEDLINE search results.

Citation tracking
Citation tracking may help identify relevant studies in MEDLINE.

Clustering
Clustering search results may help.

Filters (hedges)
MEDLINE filters, also called hedges, are an optimal Boolean combination of search terms, both textword and MeSH terms, to search articles. Many filters have been made by the Hedges Team and are available as Clinical Queries at PubMed. Filters have been criticized for being imperfect.

Filters for article types
One filter is for identifying randomized controlled trials. Many MEDLINE filters have been developed by the Hedges team supported by a grant from the National Library of Medicine. Examples include filters for randomized controlled trials and systematic reviews.

Filters for subject types
A filter have been developed for articles about kidney disease, dentistry , and about specific age ranges.

Relevancy ranking
Although MEDLINE is usually searched for exact matches using Boolean terms, relevancy ranking has been studied. In an early comparison, relevancy ranking performed well; however, the Boolean version of MEDLINE did not fully use MeSH terms.

eTBLAST uses text mining to search for similar publications.

Citation analysis or PageRank
There are conflicting results over the role of ranking results based on citation counts or PageRank. A study using Google's own PageRank found PubMed's clinical queries to be better. However, a comparative study found better results for a metric analogous to PageRank for biomedical journals based on:


 * $$\text{PageRank for the index article} = \frac{\text{the number of articles citing the index article }}{\text{the number of articles cited by the index article}}$$

Machine learning
Machine learning methods in which the search engine seeks articles that more resemble the included articles, may be more accurate than Boolean methods (see EBMSearch below).

Research methods for comparative studies
In comparing the information retrieval of search strategies, there are two experimental methods.


 * 1) If a complete test collection of articles is available that is already divided into articles of meeting inclusion criteria and articles that not meeting criteria, then each strategy is compared for its ability to successfully identify the articles meeting criteria (sensitivity) and to successfully exclude (specificity) the articles not meeting criteria. Sensitivity is also called "recall".
 * 2) If a partial test collection is available that only consists of articles meeting inclusion criteria (for example, article meeting inclusion criteria for ACP Journal Club or articles included in a systematic review of a clinical topic or articles in an annotated bibliography ), then the sensitivity is again the proportion of relevant articles identified by the strategy. However, the specificity is not computable. Instead, one of several related measures are calculated. These measures are all based on the positive predictive value (PPV) of the strategy. Analogous to PPV used in diagnostic testing, the PPV directly correlates with the prevalence of relevant articles in the collection and thus is not stable across prevalences.
 * 3) Precision is "the proportion of retrieved articles that meet criteria" and thus is the same as the PPV.
 * 4) Hit curve "is the number of important articles among the first n results."
 * 5) Number Needed to Read (NNR) is "how many papers in a journal have to be read to find one of adequate clinical quality and relevance."   Of note, the NNR has been proposed as a metric to help libraries to decide which journals to subscribe to.

Methods to access MEDLINE
There are many third party interfaces to search MEDLINE such as OVID. The National Library of Medicine's own search interface is PubMed (http://pubmed.gov).

PubMed
PubMed (http://pubmed.gov) is the National Library of Medicine's own free Internet access to MEDLINE. PubMed has been freely available since 1997.

EBM Search
EBM Search (http://www.ahsl.arizona.edu/ebmsearch/) is a federated medical search engine.

EBMSearch
EBMSearch (http://ebmsearch.org/) maintains its own copy of MEDLINE and uses machine learning to rank articles.

eTBLAST
eTBLAST uses text mining to search for similar publications.

GoPubMed
GoPubMed (http://www.GoPubMed.org/) applies social networking to MEDLINE.

HubMed
HubMed (http://www.hubmed.org/) does not maintain its own copy of MEDLILNE, but rather uses PubMed's EUtils web service to retrieve MEDLINE records stored at PubMed.

Ovid

 * Ovid Searching Tips

SUMSearch
SUMSearch (http://sumsearch.uthscsa.edu/) is a federated medical search engine. It does not maintain its own copy of MEDLINE, but rather queries PubMed and revises searches too few or too many citations are retrieved. At the same time, SUMSearch queries the National Guidelines Clearinghouse, DARE, WikiPedia, and other resources.