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of 189
pro vyhledávání: '"Ratliff, William"'
Autor:
Sandhu, Sahil, Lin, Anthony L, Brajer, Nathan, Sperling, Jessica, Ratliff, William, Bedoya, Armando D, Balu, Suresh, O'Brien, Cara, Sendak, Mark P
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 11, p e22421 (2020)
BackgroundMachine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these prod
Externí odkaz:
https://doaj.org/article/aafc3bc95de645e78f874b5e30fa397c
Autor:
Sendak, Mark P, Ratliff, William, Sarro, Dina, Alderton, Elizabeth, Futoma, Joseph, Gao, Michael, Nichols, Marshall, Revoir, Mike, Yashar, Faraz, Miller, Corinne, Kester, Kelly, Sandhu, Sahil, Corey, Kristin, Brajer, Nathan, Tan, Christelle, Lin, Anthony, Brown, Tres, Engelbosch, Susan, Anstrom, Kevin, Elish, Madeleine Clare, Heller, Katherine, Donohoe, Rebecca, Theiling, Jason, Poon, Eric, Balu, Suresh, Bedoya, Armando, O'Brien, Cara
Publikováno v:
JMIR Medical Informatics, Vol 8, Iss 7, p e15182 (2020)
BackgroundSuccessful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. ObjectiveThis study aims to report a quality improvement effort to integr
Externí odkaz:
https://doaj.org/article/65aad1bd03f649c09ba7fe1c7e2f793a
Autor:
Sendak, Mark, Sirdeshmukh, Gaurav, Ochoa, Timothy, Premo, Hayley, Tang, Linda, Niederhoffer, Kira, Reed, Sarah, Deshpande, Kaivalya, Sterrett, Emily, Bauer, Melissa, Snyder, Laurie, Shariff, Afreen, Whellan, David, Riggio, Jeffrey, Gaieski, David, Corey, Kristin, Richards, Megan, Gao, Michael, Nichols, Marshall, Heintze, Bradley, Knechtle, William, Ratliff, William, Balu, Suresh
The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical
Externí odkaz:
http://arxiv.org/abs/2208.02670
Autor:
Ratliff, William Charles
During the Spring 2020 semester, the COVID19 pandemic forced colleges and universities to adopt remote learning for an emergency remote learning period (ELP) from March to May 2020. The current work utilized a phenomenological approach to understandi
Autor:
Amin, Krunal D., Weissler, Elizabeth Hope, Ratliff, William, Sullivan, Alexander E., Holder, Tara A., Bury, Cathleen, Francis, Samuel, Theiling, Brent Jason, Hintze, Bradley, Gao, Michael, Nichols, Marshall, Balu, Suresh, Jones, William Schuyler, Sendak, Mark
Publikováno v:
In Annals of Emergency Medicine August 2024 84(2):118-127
Autor:
Xia, Meng, Kheterpal, Meenal K., Wong, Samantha C., Park, Christine, Ratliff, William, Carin, Lawrence, Henao, Ricardo
We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single
Externí odkaz:
http://arxiv.org/abs/2104.02652
Autor:
Sendak, Mark, Elish, Madeleine, Gao, Michael, Futoma, Joseph, Ratliff, William, Nichols, Marshall, Bedoya, Armando, Balu, Suresh, O'Brien, Cara
Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less focus on real world implementation and the associated challenges to accur
Externí odkaz:
http://arxiv.org/abs/1911.08089
Autor:
Sandhu, Sahil, Sendak, Mark P., Ratliff, William, Knechtle, William, Fulkerson, William J., Jr., Balu, Suresh
Publikováno v:
In Patterns 14 April 2023 4(4)
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