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10.1093/jamia/ocaa271 http://scihub22266oqcxt.onion/10.1093/jamia/ocaa271 33197268!7717324!33197268     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (1): 132-137 Nephropedia Template TP {{tp|p=33197268|t=2021. An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature |pdf=|usr=}}{{33197268}} gab.com Text An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33197268 #FOAvip The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literature and search engines to query such data. While most search engine research is performed in academia with rigorous evaluation, major commercial companies dominate the web search market. Thus, it is expected that commercial pandemic-specific search engines will gain much higher traction than academic alternatives, leading to questions about the empirical performance of these tools. This paper seeks to empirically evaluate two commercial search engines for COVID-19 (Google and Amazon) in comparison with academic prototypes evaluated in the TREC-COVID task. We performed several steps to reduce bias in the manual judgments to ensure a fair comparison of all systems. We find the commercial search engines sizably underperformed those evaluated under TREC-COVID. This has implications for trust in popular health search engines and developing biomedical search engines for future health crises. Twit Text FOAVip An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33197268 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33197268 #FOAvip English Wikipedia <ref>{{Cite pmid|33197268}}</ref> An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature #MMPMID33197268 Soni S; Roberts K J Am Med Inform Assoc 2021[Jan]; 28 (1): 132-137 PMID33197268show ga The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literature and search engines to query such data. While most search engine research is performed in academia with rigorous evaluation, major commercial companies dominate the web search market. Thus, it is expected that commercial pandemic-specific search engines will gain much higher traction than academic alternatives, leading to questions about the empirical performance of these tools. This paper seeks to empirically evaluate two commercial search engines for COVID-19 (Google and Amazon) in comparison with academic prototypes evaluated in the TREC-COVID task. We performed several steps to reduce bias in the manual judgments to ensure a fair comparison of all systems. We find the commercial search engines sizably underperformed those evaluated under TREC-COVID. This has implications for trust in popular health search engines and developing biomedical search engines for future health crises. |*COVID-19[MESH] |*Deep Learning[MESH] |*Information Systems[MESH] |*Search Engine[MESH] |Humans[MESH] |Information Storage and Retrieval/*methods[MESH]An evaluation of two commercial deep learning-based information retrie(epmc).pdf DeepDyve Pubget Overpricing