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10.1016/j.artmed.2021.102053 http://scihub22266oqcxt.onion/10.1016/j.artmed.2021.102053 33875160!ä!33875160 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Artif+Intell+Med 2021 ; 114 (ä): 102053 Nephropedia Template TP {{tp|p=33875160|t=2021. NewsMeSH: A new classifier designed to annotate health news with MeSH headings |pdf=|usr=}}{{33875160}} gab.com Text NewsMeSH: A new classifier designed to annotate health news with MeSH headings JO: Artif Intell Med http://www.kidney.de/pget.php?&tw=1&pmid=33875160 #FOAvip MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar. Twit Text FOAVip NewsMeSH: A new classifier designed to annotate health news with MeSH headings ????Artif Intell Med http://www.kidney.de/pget.php?&tw=1&pmid=33875160 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33875160 #FOAvip English Wikipedia <ref>{{Cite pmid|33875160}}</ref> NewsMeSH: A new classifier designed to annotate health news with MeSH headings #MMPMID33875160 Pita Costa J; Rei L; Stopar L; Fuart F; Grobelnik M; Mladenic D; Novalija I; Staines A; Paakkonen J; Konttila J; Bidaurrazaga J; Belar O; Henderson C; Epelde G; Gabarain MA; Carlin P; Wallace J Artif Intell Med 2021[Apr]; 114 (ä): 102053 PMID33875160show ga MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar. |*Medical Subject Headings[MESH] |Big Data[MESH] |COVID-19/epidemiology[MESH] |Classification[MESH] |Diabetes Mellitus/epidemiology[MESH] |Health Communication/*standards[MESH] |Humans[MESH] |MEDLINE/*organization & administration/standards[MESH] |Mental Health/statistics & numerical data[MESH] |Research/*organization & administration[MESH] |SARS-CoV-2[MESH]NewsMeSH: A new classifier designed to annotate health news with MeSH (epmc).pdf DeepDyve Pubget Overpricing