BACKGROUND AND OBJECTIVES: Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we develo...
BACKGROUND: Traditional methods of developing predictive models in inflammatory bowel diseases (IBD) rely on using statistical regression approaches to deriving clinical scores such as the Crohn's disease (CD) activity index. However, traditional app...
BACKGROUND: It is unclear whether data-driven machine learning models, which are trained on large epidemiological cohorts, may improve prediction of comorbidities in people living with human immunodeficiency virus (HIV).
OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1)...
BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically i...
BACKGROUND: Currently, only a fraction of patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICIs) experience a durable clinical benefit (DCB). According to NCCN guidelines, Programmed death-ligand 1 (PD-L1) e...
IMPORTANCE: Accurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.
High dimensional neuroimaging datasets and machine learning have been used to estimate and predict domain-specific cognition, but comparisons with simpler models composed of easy-to-measure variables are limited. Regularization methods in particular ...
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