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Detecting Biomedical Named Entities in COVID-19 Texts.

, and . Healthcare AI and COVID-19 Workshop, volume 184 of Proceedings of Machine Learning Research, page 117-126. PMLR, (2022)

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Detecting Biomedical Named Entities in COVID-19 Texts., and . Healthcare AI and COVID-19 Workshop, volume 184 of Proceedings of Machine Learning Research, page 117-126. PMLR, (2022)A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities., and . CoRR, (2022)A non-homogeneous approach to simulating the spread of disease in a pandemic outbreak., , and . WSC, page 2941. WSC, (2008)Incorporating healthcare systems in pandemic models., , and . WSC, page 2230-2236. IEEE, (2010)Accounting for Individual Behaviors in a Pandemic Disease Spread Model., , and . WSC, page 1977-1985. IEEE, (2009)A Question-Answering System on COVID-19 Scientific Literature., , and . COMPSAC, page 1331-1336. IEEE, (2022)Integral projection methods for block motion estimation., and . VCIP, volume 2094 of SPIE Proceedings, SPIE, (1993)On complex behavior and exchange rate dynamics, and . Chaos, Solitons & Fractals, 18 (3): 503--523 (October 2003)From Trauma in Austere Environments to Combat or Medical School: How Blended Hyper-Realism in the Real and Virtual Worlds Can Better Prepare Surgeons., , , , , , , , and . MMVR, volume 196 of Studies in Health Technology and Informatics, page 233-237. IOS Press, (2014)Lasagna plots: a saucy alternative to spaghetti plots., , , , , and . Epidemiology (Cambridge, Mass.), 21 (5): 621-5 (September 2010)5910<m:linebreak></m:linebreak>GR: HL075078/HL/NHLBI NIH HHS/United States; GR: HL086862/HL/NHLBI NIH HHS/United States; GR: HL089467/HL/NHLBI NIH HHS/United States; GR: K25EB003491/EB/NIBIB NIH HHS/United States; GR: R01AG10785/AG/NIA NIH HHS/United States; GR: R01NS060910/NS/NINDS NIH HHS/United States; JID: 9009644; NIHMS225391; OID: NLM: NIHMS225391 Available on 09/01/11; OID: NLM: PMC2937254 Available on 09/01/11; ppublish;.