Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Katherine J Forsythe"'
Autor:
Peter J Carrick, Katherine J Forsythe
Publikováno v:
PLoS ONE, Vol 15, Iss 7, p e0236550 (2020)
The idea that biodiversity is necessary in order for ecosystems to function properly has long been used as a basic argument for the conservation of species, and has led to an abundance of research exploring the relationships between species richness
Externí odkaz:
https://doaj.org/article/a1e1e2e4e5da4b7683824935213e244e
Autor:
Katherine J Forsythe, Bunyarit Meksiriporn, Erin A. Stephens, Pengbo Zhou, Connor Monticello, Matthew P. DeLisa, Morgan B. Ludwicki, Mingji Li, Andreas Plückthun, Tianzheng Ye, Lutz Kummer
Publikováno v:
ACS synthetic biology. 10(9)
Ubiquibodies (uAbs) are a customizable proteome editing technology that utilizes E3 ubiquitin ligases genetically fused to synthetic binding proteins to steer otherwise stable proteins of interest (POIs) to the 26S proteasome for degradation. The abi
Autor:
Gordon D. Schiff, Katherine J. Forsythe, Mary G. Amato, Kenneth H. Lai, Tewodros Eguale, Samuel J Karmiy, Bruce L. Lambert, Alejandra Salazar, Adam Wright, Lynn A. Volk, David Liebovitz
Publikováno v:
American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists. 76(13)
Purpose To examine the extent to which outpatient clinicians currently document drug indications in prescription instructions. Methods Free-text sigs were extracted from all outpatient prescriptions generated by the computerized prescriber order entr
Autor:
Joanna Abraham, William L. Galanter, Lynn A. Volk, Sarah K. McCord, Alejandra Salazar, Kevin W. Kron, Gordon D. Schiff, Katherine J. Forsythe, Adam Wright, Mary G. Amato, Tewodros Eguale, Pamela M. Garabedian, Aaron W. Nathan, Isabella Newbury, Sara Myers, David W. Bates
Publikováno v:
JAMA Network Open
Key Points Question Is a redesigned electronic prescribing workflow to better support the incorporation of the indication in the outpatient prescribing process associated with reduced errors and improved clinician experience? Findings This quality im
Autor:
Rosa Rodriguez-Monguio, Maria McGurrin, Lynn A. Volk, Enrique Seoane-Vazquez, Sara Myers, David W. Bates, Ronen Rozenblum, Gordon D. Schiff, Katherine J. Forsythe, Deborah H. Williams
Publikováno v:
Joint Commission journal on quality and patient safety. 46(1)
Background Clinical decision support (CDS) alerting tools can identify and reduce medication errors. However, they are typically rule-based and can identify only the errors previously programmed into their alerting logic. Machine learning holds promi