AIMC Topic: Cohort Studies

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Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments.

Pediatric emergency care
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...

Deep learning conventional learning algorithms for clinical prediction in Crohn's disease: A proof-of-concept study.

World journal of gastroenterology
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...

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Shock (Augusta, Ga.)
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...

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images.

Journal for immunotherapy of cancer
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...

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction.

JAMA cardiology
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.

Machine learning-based estimation of cognitive performance using regional brain MRI markers: the Northern Manhattan Study.

Brain imaging and behavior
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 ...