AIMC Topic: Retrospective Studies

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Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Echocardiography (Mount Kisco, N.Y.)
BACKGROUND: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during init...

Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI.

European radiology
OBJECTIVE: To investigate the value of machine learning (ML)-based high-dimensional quantitative texture analysis (qTA) on T2-weighted magnetic resonance imaging (MRI) in predicting response to somatostatin analogues (SA) in acromegaly patients with ...

Robot-assisted intravertebral augmentation corrects local kyphosis more effectively than a conventional fluoroscopy-guided technique.

Journal of neurosurgery. Spine
OBJECTIVEIntravertebral augmentation (IVA) is a reliable minimally invasive technique for treating Magerl type A vertebral body fractures. However, poor correction of kyphotic angulation, the risk of cement leakage, and significant exposure to radiat...

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
OBJECTIVES: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospital-level care could triage patients more efficiently to high- or low-resource ED tracks. Existing t...