The exponential growth of the number of medical [[publication]]s makes it more and more difficult for the clinical practitioner to keep track of relevant scientific progress. Computer aided screening and analysis might help to overview and classify the content of medical publications more efficiently. The new wave of enthusiasm for [[machine learning algorithm]]s has launched several new possibilities to analyze and categorize [[document]]s, ranging from sports news to hospital [[discharge]] summaries. Search [[algorithm]]s for the medical [[literature]], i.e. [[PubMed]] are based on keywords and full text search. The U.S. National Library of Medicine and National Institutes of Health have made great effort to implement semantics into their data structure 
((UMLS terminology services - A service of the U.S.
national library of medicine, National Institutes of
Health, https://uts.nlm.nih.gov/home.html/. Accessed
4 April 2020.)).

Most of the concepts base on the [[W3C]] ideas and standards
((W3C Standards, http://www.w3.org/standards/. Accessed
4 April 2020)).

Concepts are identified unambiguously by a [[Uniform Resource Identifier]]s (URI). This approach allows for retrieving medical literature relating defined key words efficiently, however, it remains impossible to automatically evaluate the [[relevance]] of [[publication]]s. [[Machine learning]] has enabled efficient text classification for various data sources such as [[tweet]]s, customer complaints, sports news and movie reviews