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10.1016/j.jbi.2016.11.004 http://scihub22266oqcxt.onion/10.1016/j.jbi.2016.11.004 C5588694!5588694 !27866001     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27866001 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Biomed+Inform 2017 ; 65 (ä): 46-57 Nephropedia Template TP {{tp|p=27866001 |t=2017. Generating disease-pertinent treatment vocabularies from MEDLINE citations |pdf=|usr=}}{{27866001 }} gab.com Text Generating disease-pertinent treatment vocabularies from MEDLINE citations JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=27866001 #FOAvip OBJECTIVE: Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. METHODS: A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. RESULTS: The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines. CONCLUSIONS: The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature. Twit Text FOAVip Generating disease-pertinent treatment vocabularies from MEDLINE citations ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=27866001 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27866001 #FOAvip English Wikipedia <ref>{{Cite pmid|27866001 }}</ref> Generating disease-pertinent treatment vocabularies from MEDLINE citations #MMPMID27866001 Wang L ; Del Fiol G ; Bray BE ; Haug PJ J Biomed Inform 2017[Jan]; 65 (ä): 46-57 PMID27866001 show ga OBJECTIVE: Healthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies. METHODS: A pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches. RESULTS: The pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p=0.53). However, the pipeline-based system had significantly better performance than the two baselines. CONCLUSIONS: The pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature. |*MEDLINE [MESH] |*Semantics [MESH] |*Vocabulary, Controlled [MESH] |Automation [MESH] |Humans [MESH] |Information Storage and Retrieval [MESH]Generating disease-pertinent treatment vocabularies from MEDLINE citat(epmc).pdf DeepDyve Pubget Overpricing