Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Fang Liz Zhou"'
Autor:
Tao Wang, Rong Lu, Sunny Lai, Joan H Schiller, Fang Liz Zhou, Bo Ci, Stacy Wang, Xiaohan Gao, Bo Yao, David E Gerber, David H Johnson, Guanghua Xiao, Yang Xie
Publikováno v:
Cancer Informatics, Vol 18 (2019)
Importance: Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials. Objective: The primary objective is to provide an updated and accurate prognostic model for predicting the survival of adv
Externí odkaz:
https://doaj.org/article/5db8068716cb42789265ca92793e01ff
Autor:
Stewart B. Harris, Kamel Mohammedi, Monica Bertolini, John White, Valery Walker, Fang Liz Zhou, John E. Anderson, Jochen Seufert
Publikováno v:
Diabetes, Obesity and Metabolism. 25:1408-1412
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2da2dbdc898bba0ac5faee32f8a5e0a7
https://doi.org/10.1111/dom.14973
https://doi.org/10.1111/dom.14973
Autor:
null Stewart B Harris, null Kamel Mohammedi, null Monica Bertolini, null John White, null Valery Walker, null Fang Liz Zhou, null John E Anderson, null Jochen Seufert
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20ec98196a8126539712455a4c25d4c8
https://doi.org/10.1111/dom.14973/v2/response1
https://doi.org/10.1111/dom.14973/v2/response1
Autor:
null Angela Ibald‐Mulli, null Jochen Seufert, null Julia M. Grimsmann, null Markus Laimer, null Peter Bramlage, null Alexandre Civet, null Margot Blanchon, null Simon Gosset, null Alexandre Templier, null W. Dieter Paar, null Fang Liz Zhou, null Stefanie Lanzinger
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c99504a2e6019e1241751db1433a426b
https://doi.org/10.1111/dom.15039/v2/response1
https://doi.org/10.1111/dom.15039/v2/response1
Autor:
Chuang-Chung Lee, Kristen Sharma, Zoran Doder, Cliona Molony, Fang Liz Zhou, Evgeny Zalmover, Lin Li, Chuntao Wu, Juhaeri Juhaeri
Publikováno v:
Pharmacoepidemiology and Drug Safety
Purpose To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA. Methods This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4edbfa478c2ef9962d945505f82f72c
http://europepmc.org/articles/PMC8049019
http://europepmc.org/articles/PMC8049019
Autor:
Sumit Verma, Fang Liz Zhou, Steve Edelman, Michael J Davies, Sachin A. Paranjape, Ronald Preblick, Vijay N. Joish
Publikováno v:
PharmacoEconomics Open
Background and objectives The burden imposed by cardiovascular disease (CVD) on patients with type 1 diabetes (T1D) in the US has not been thoroughly addressed. In a retrospective observational analysis of the Optum® Clinformatics™ Data Mart datab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b7dd3e00377de04c6446d8a4dadbc32
https://doi.org/10.1007/s41669-019-00192-9
https://doi.org/10.1007/s41669-019-00192-9
Autor:
Phillip R. Hunt, Steven V. Edelman, Jeremy Pettus, Katie Mercaldi, Sachin Paranjape, Fang Liz Zhou, Kellee M. Miller, Ronald Preblick, Leah Shepherd
Publikováno v:
Diabetes, Obesity & Metabolism
Diabetes, obesity & metabolism, vol 22, iss 4
Diabetes, obesity & metabolism, vol 22, iss 4
Author(s): Pettus, Jeremy H; Zhou, Fang Liz; Shepherd, Leah; Mercaldi, Katie; Preblick, Ronald; Hunt, Phillip R; Paranjape, Sachin; Miller, Kellee M; Edelman, Steven V | Abstract: AimsTo use electronic health record data from real-world clinical prac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eda0334a948d2d2201e92a7bf65f6776
http://europepmc.org/articles/PMC7079022
http://europepmc.org/articles/PMC7079022
Autor:
Ambika Gopalakrishnan Unnikrishnan, Vijay Viswanathan, Fang Liz Zhou, Lichen Hao, Prithvi Kamath, Monica Bertolini, Jose Fernando Botero, Leonardo Mancillas-Adame
Publikováno v:
Diabetes therapy : research, treatment and education of diabetes and related disorders. 13(5)
My Dose Coach (MDC) is a US Food and Drug Administration-approved digital smartphone application designed to help users with type 2 diabetes (T2D) titrate their basal insulin (BI) according to a clinician-prescribed individualized titration plan. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e947a6365f95e12b59b7d8930d17ee10
https://pubmed.ncbi.nlm.nih.gov/35316509
https://pubmed.ncbi.nlm.nih.gov/35316509
Autor:
Jeremy Pettus, Fang Liz Zhou, Ronan Roussel, Timothy S. Bailey, Björn Eliasson, Javier Jimenez, Luigi F. Meneghini, Rachele Berria, Irene Hramiak, Zsolt Bosnyak, Jukka Westerbacka
Publikováno v:
Diabetes Therapy
Introduction The LIGHTNING study applied conventional and advanced analytic approaches to model, predict, and compare hypoglycemia rates of people with type 2 diabetes (T2DM) on insulin glargine 300 U/ml (Gla-300) with those on first-generation (insu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b643957de099f12e20dc626c42de23c
https://doi.org/10.1007/s13300-019-0568-8
https://doi.org/10.1007/s13300-019-0568-8