Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Busola Oronti"'
Publikováno v:
BMJ Open, Vol 12, Iss 4 (2022)
Objective To identify and assess the use of technologies, including mobile health technology, internet of things (IoT) devices and artificial intelligence (AI) in hypertension healthcare in sub-Saharan Africa (SSA).Design Systematic review.Data sourc
Externí odkaz:
https://doaj.org/article/dfa0582415ac43b58126c2fe3eb94cfc
Autor:
Alessia Maccaro, Davide Piaggio, Iyabosola Busola Oronti, Marius Vignigbé, Antoinette Gbokli, Roch Houngnihin, Leandro Pecchia
Publikováno v:
Frontiers in Medicine, Vol 9 (2022)
IntroductionThis article aims at investigating social engagement in the fight against the COVID-19 pandemic in low-resource settings (LRSs). In particular, it focuses on Benin (Sub-Saharan Africa), and reports the results of a field study that invest
Externí odkaz:
https://doaj.org/article/8022d51a00b845239bb75eb1540cedb5
Publikováno v:
BMJ open. 12(4)
ObjectiveTo identify and assess the use of technologies, including mobile health technology, internet of things (IoT) devices and artificial intelligence (AI) in hypertension healthcare in sub-Saharan Africa (SSA).DesignSystematic review.Data sources
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2a37e36b2891279d49c14d9f3fb935b
https://pubmed.ncbi.nlm.nih.gov/35383086
https://pubmed.ncbi.nlm.nih.gov/35383086
Publikováno v:
Electronics; Volume 11; Issue 23; Pages: 3996
Congestive heart failure (CHF) is one of the most debilitating cardiac disorders. It is a costly disease in terms of both lives and financial outlays, given the high rate of hospital re-admissions and mortality. Heart failure (HF) is notoriously diff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4c711bad449747f866dc91abe188203
https://doi.org/10.3390/electronics11233996
https://doi.org/10.3390/electronics11233996
Autor:
Tim Dong, Iyabosola Busola Oronti, Shubhra Sinha, Alberto Freitas, Bing Zhai, Jeremy Chan, Daniel P. Fudulu, Massimo Caputo, Gianni D. Angelini
Publikováno v:
Bioengineering, Vol 11, Iss 10, p 1039 (2024)
Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, is limi
Externí odkaz:
https://doaj.org/article/dfc2eb31965a4cc6aba501c217b1e6dc