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10.1093/jamia/ocaa116 http://scihub22266oqcxt.onion/10.1093/jamia/ocaa116 32620975!7337747!32620975     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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2020 ; 27 (8): 1310-1315 Nephropedia Template TP {{tp|p=32620975|t=2020. Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource |pdf=|usr=}}{{32620975}} gab.com Text Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=32620975 #FOAvip OBJECTIVE: To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. MATERIALS AND METHODS: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. RESULTS: We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. CONCLUSION: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings. Twit Text FOAVip Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=32620975 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32620975 #FOAvip English Wikipedia <ref>{{Cite pmid|32620975}}</ref> Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource #MMPMID32620975 Sarker A; Lakamana S; Hogg-Bremer W; Xie A; Al-Garadi MA; Yang YC J Am Med Inform Assoc 2020[Aug]; 27 (8): 1310-1315 PMID32620975show ga OBJECTIVE: To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. MATERIALS AND METHODS: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. RESULTS: We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. CONCLUSION: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings. |*Coronavirus Infections/complications/diagnosis[MESH] |*Pandemics[MESH] |*Pneumonia, Viral/complications/diagnosis[MESH] |*Self Report[MESH] |*Social Media[MESH] |*Symptom Assessment[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |Data Mining[MESH] |Humans[MESH] |Natural Language Processing[MESH]Self-reported COVID-19 symptoms on Twitter: an analysis and a research(epmc).pdf DeepDyve Pubget Overpricing