Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Sheikh S. Abdullah"'
Autor:
Sheikh S. Abdullah, Neda Rostamzadeh, Flory T. Muanda, Eric McArthur, Matthew A. Weir, Jessica M. Sontrop, Richard B. Kim, Sedig Kamran, Amit X. Garg
Publikováno v:
Canadian Journal of Kidney Health and Disease, Vol 11 (2024)
Background: Safety issues are detected in about one third of prescription drugs in the years following regulatory agency approval. Older adults, especially those with chronic kidney disease, are at particular risk of adverse reactions to prescription
Externí odkaz:
https://doaj.org/article/968e324d9f634da3ac9970efd1498e04
Autor:
Ahmed A. Al-Jaishi, Monica Taljaard, Melissa D. Al-Jaishi, Sheikh S. Abdullah, Lehana Thabane, P. J. Devereaux, Stephanie N. Dixon, Amit X. Garg
Publikováno v:
Systematic Reviews, Vol 11, Iss 1, Pp 1-10 (2022)
Abstract Background Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making
Externí odkaz:
https://doaj.org/article/2eed42a4cd0d444e9fb02f8aa95b74e3
Publikováno v:
Informatics, Vol 9, Iss 1, p 17 (2022)
Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and
Externí odkaz:
https://doaj.org/article/272925043b0949e595064b6b0384bf30
Publikováno v:
Information, Vol 12, Iss 9, p 344 (2021)
The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially w
Externí odkaz:
https://doaj.org/article/42c744238c664c0fbbe44e0857756b97
Publikováno v:
Data, Vol 6, Iss 8, p 85 (2021)
Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient ca
Externí odkaz:
https://doaj.org/article/7a161eb2befd4cb1b8791f828ab40f5e
Publikováno v:
Informatics, Vol 8, Iss 1, p 12 (2021)
The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing dem
Externí odkaz:
https://doaj.org/article/74c4649ca15640f09223e0d3227c58e4
Publikováno v:
Information, Vol 11, Iss 8, p 386 (2020)
Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early
Externí odkaz:
https://doaj.org/article/2c3cafdb54ad406d929376ff46b7ac88
Publikováno v:
Informatics, Vol 7, Iss 2, p 17 (2020)
Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques
Externí odkaz:
https://doaj.org/article/7653556f0f6942f5aef077627c88c3a5
Autor:
Sheikh S. Abdullah, Neda Rostamzadeh, Kamran Sedig, Daniel J. Lizotte, Amit X. Garg, Eric McArthur
Publikováno v:
Informatics, Vol 7, Iss 2, p 18 (2020)
One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated m
Externí odkaz:
https://doaj.org/article/6dea985cd2da45379626253802524f96
Publikováno v:
Multimodal Technologies and Interaction, Vol 4, Iss 1, p 7 (2020)
Electronic health records (EHRs) can be used to make critical decisions, to study the effects of treatments, and to detect hidden patterns in patient histories. In this paper, we present a framework to identify and analyze EHR-data-driven tasks and a
Externí odkaz:
https://doaj.org/article/cbc53e94a87e4c4788059f116cbc8b88