Ghadeer O. Ghosheh
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View article: CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks
CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation Using Graph Neural Networks Open
View article: Temporal dynamics unleashed: Elevating variational graph attention
Temporal dynamics unleashed: Elevating variational graph attention Open
View article: Understanding Missingness in Time-series Electronic Health Records for Individualized Representation
Understanding Missingness in Time-series Electronic Health Records for Individualized Representation Open
With the widespread of machine learning models for healthcare applications, there is increased interest in building applications for personalized medicine. Despite the plethora of proposed research for personalized medicine, very few focus…
View article: CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation using Graph Neural Networks
CliqueFluxNet: Unveiling EHR Insights with Stochastic Edge Fluxing and Maximal Clique Utilisation using Graph Neural Networks Open
Electronic Health Records (EHRs) play a crucial role in shaping predictive healthcare models, yet they encounter challenges such as significant data gaps and class imbalances. Traditional Graph Neural Networks (GNNs) approaches have limita…
View article: A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data
A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data Open
The burden of diseases is rising worldwide, with unequal treatment efficacy for patient populations that are underrepresented in clinical trials. Healthcare, however, is driven by the average population effect of medical treatments and, th…
View article: IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records
IGNITE: Individualized GeNeration of Imputations in Time-series Electronic health records Open
Electronic Health Records present a valuable modality for driving personalized medicine, where treatment is tailored to fit individual-level differences. For this purpose, many data-driven machine learning and statistical models rely on th…
View article: A Survey of Generative Adversarial Networks for Synthesizing Structured Electronic Health Records
A Survey of Generative Adversarial Networks for Synthesizing Structured Electronic Health Records Open
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and point of care applications; however, many challenges such as data privacy concerns impede its optimal utilization. Deep generative models, particular…
View article: Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits
Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits Open
Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper…
View article: Synthesizing Electronic Health Records for Predictive Models in Low-Middle-Income Countries (LMICs)
Synthesizing Electronic Health Records for Predictive Models in Low-Middle-Income Countries (LMICs) Open
The spread of machine learning models, coupled with by the growing adoption of electronic health records (EHRs), has opened the door for developing clinical decision support systems. However, despite the great promise of machine learning f…
View article: Synthesizing Mixed-type Electronic Health Records using Diffusion Models
Synthesizing Mixed-type Electronic Health Records using Diffusion Models Open
Electronic Health Records (EHRs) contain sensitive patient information, which presents privacy concerns when sharing such data. Synthetic data generation is a promising solution to mitigate these risks, often relying on deep generative mod…
View article: Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic
Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic Open
View article: Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study
Clinical prediction system of complications among COVID-19 patients: a development and validation retrospective multicentre study Open
Existing prognostic tools mainly focus on predicting the risk of mortality among patients with coronavirus disease 2019. However, clinical evidence suggests that COVID-19 can result in non-mortal complications that affect patient prognosis…