Zobrazeno 1 - 10
of 66
pro vyhledávání: '"William G. Baxt"'
Publikováno v:
The Journal of Emergency Medicine. 39:506-511
Background: Boarding of admitted patients in the Emergency Department (ED) is common and is associated with poor patient outcomes. Objectives: We sought to estimate the magnitude of and trends for ED boarding in the US. Methods: We used the 2003–20
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::242010e95274bbc58dc67e8134e9540b
https://doi.org/10.1016/j.jemermed.2008.04.035
https://doi.org/10.1016/j.jemermed.2008.04.035
Autor:
Anna Marie Chang, Mark G. Weiner, Frances S. Shofer, Harold Litt, Judd E. Hollander, Marie Synnestvedt, William G. Baxt
Publikováno v:
Academic Emergency Medicine. 15:649-655
Objectives: Small studies have shown that a negative computed tomography coronary angiogram (CTA) in low-risk chest pain patients predicts a low rate of 30-day adverse events. The authors hypothesized that an immediate CTA strategy would be as effect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df53114d609ad55226ec2bae37bfb620
https://doi.org/10.1111/j.1553-2712.2008.00159.x
https://doi.org/10.1111/j.1553-2712.2008.00159.x
DNA-RNA hybridization was used to explore whether human neoplasias contain RNA molecules having sequence homologies to those of the RNA tumor viruses known to cause similar diseases in animals. The pattern of specific RNAs found in the human tumors s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27f87f225d91c4689d0c723acdd5cd7e
https://doi.org/10.1159/000397514
https://doi.org/10.1159/000397514
Autor:
Keara L. Sease, Frances S. Shofer, Frank D. Sites, Dina M. Sparano, William G. Baxt, Judd E. Hollander
Publikováno v:
Annals of Emergency Medicine. 44:199-205
Study objective Neural networks can risk-stratify emergency department (ED) patients with potential acute coronary syndromes with a high specificity, potentially facilitating ED discharge of patients to home. We hypothesized that the use of "real-tim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5485f51f6b3ca3f4548a3cc6d0f43764
https://doi.org/10.1016/j.annemergmed.2004.02.037
https://doi.org/10.1016/j.annemergmed.2004.02.037
Publikováno v:
Annals of Emergency Medicine. 40:575-583
Study objective: Chest pain is the second most common chief complaint presented to the emergency department. Although the causes of chest pain span the clinical spectrum from the trivial to the life threatening, it is often difficult to identify whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8256673068d82be3f22aa59bd052e7cc
https://doi.org/10.1067/mem.2002.129171
https://doi.org/10.1067/mem.2002.129171
Publikováno v:
Annals of Emergency Medicine. 39:366-373
Study Objective: Accurate identification of the presence of acute myocardial infarction in adult patients who present to the emergency department with anterior chest pain remains elusive. The artificial neural network is a powerful nonlinear statisti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8287355ca69138728b8db5db30f58b36
https://doi.org/10.1067/mem.2002.122705
https://doi.org/10.1067/mem.2002.122705
Publikováno v:
Annals of Emergency Medicine. 32:310-317
Study objective: To determine whether a fictitious manuscript into which purposeful errors were placed could be used as an instrument to evaluate peer reviewer performance. Methods: An instrument for reviewer evaluation was created in the form of a f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4738facdd724becebb150b73cec456db
https://doi.org/10.1016/s0196-0644(98)70006-x
https://doi.org/10.1016/s0196-0644(98)70006-x
Autor:
William G. Baxt, Halbert White
Publikováno v:
Neural Computation. 7:624-638
The artificial neural network has been successfully applied to a broad range of clinical settings (Widrow and Hoff 1960; Rumelhart et al. 1986; McClelland et al. 1988; Weigend et al. 1990; Hudson et al. 1988; Smith et al. 1988; Saito and Nakano 1988;
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f24cbd0d0f6b27a8b506cb9dd96dc18
https://doi.org/10.1162/neco.1995.7.3.624
https://doi.org/10.1162/neco.1995.7.3.624
Autor:
William G. Baxt
Publikováno v:
Medical Decision Making. 14:217-222
An artificial neural network trained to identify the presence of myocardial infarction has been shown to function with a high degree of accuracy. The effects on network diagnosis of some of the clinical input variables used by this network have previ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81c2f0dd468e35c85c10789f94b75fd8
https://doi.org/10.1177/0272989x9401400303
https://doi.org/10.1177/0272989x9401400303
Autor:
William G. Baxt
Publikováno v:
Neural Computing & Applications. 1:176-182
To determine whether an artificial neural network trained to recognize the presence of acute myocardial infarction makes clinical decisions based on nonlinear relationships it establishes between inputted information, a linear and nonlinear network w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa874a8820f001cd66983b40b859575f
https://doi.org/10.1007/bf01414944
https://doi.org/10.1007/bf01414944