Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Armando D Bedoya"'
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background Electronic Health Records (EHR) are widely used to develop clinical prediction models (CPMs). However, one of the challenges is that there is often a degree of informative missing data. For example, laboratory measures are typical
Externí odkaz:
https://doaj.org/article/146985da68274851924e6741a8380af1
Autor:
Cara O’Brien, Benjamin A. Goldstein, Yueqi Shen, Matthew Phelan, Curtis Lambert, Armando D. Bedoya, Rebecca C. Steorts
Publikováno v:
MDM Policy & Practice, Vol 5 (2020)
Background. Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing,
Externí odkaz:
https://doaj.org/article/6dceb46c9b9c4a8885687ddd240988e5
Publikováno v:
Pulmonary Therapy. 8:327-331
The disease origins of idiopathic pulmonary fibrosis (IPF), which occurs at higher rates in certain races/ethnicities, are not understood. The highest rates occur in white persons of European descent, particularly those with light skin, who are also
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb880b492de3abbb0b2119186ef712c5
https://doi.org/10.1007/s41030-022-00198-5
https://doi.org/10.1007/s41030-022-00198-5
Autor:
Armando D Bedoya, Nicoleta J Economou-Zavlanos, Benjamin A Goldstein, Allison Young, J Eric Jelovsek, Cara O’Brien, Amanda B Parrish, Scott Elengold, Kay Lytle, Suresh Balu, Erich Huang, Eric G Poon, Michael J Pencina
Publikováno v:
J Am Med Inform Assoc
Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::438c8c469f1e055e33656acaceda8a98
https://pubmed.ncbi.nlm.nih.gov/35641123
https://pubmed.ncbi.nlm.nih.gov/35641123
Autor:
Sahil Sandhu, Anthony L Lin, Nathan Brajer, Jessica Sperling, William Ratliff, Armando D Bedoya, Suresh Balu, Cara O'Brien, Mark P Sendak
BACKGROUND Machine 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 pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad14c8d7e380b0d17ae98589eceb9999
https://doi.org/10.2196/preprints.22421
https://doi.org/10.2196/preprints.22421
