AIMC Topic: Registries

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An ontology-based rare disease common data model harmonising international registries, FHIR, and Phenopackets.

Scientific data
Although rare diseases (RDs) affect over 260 million individuals worldwide, low data quality and scarcity challenge effective care and research. This work aims to harmonise the Common Data Set by European Rare Disease Registry Infrastructure, Health ...

Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning.

Journal of neuroscience methods
BACKGROUND AND PURPOSE: Mechanical Thrombectomy (MT) has recently become the standard of care for anterior circulation stroke with large vessel occlusion, but predictive factors of successful MT are still not clearly defined. To tailor treatment indi...

Machine learning prediction of in-hospital mortality and external validation in patients with cardiogenic shock: the RESCUE score.

Revista espanola de cardiologia (English ed.)
INTRODUCTION AND OBJECTIVES: Despite advances in mechanical circulatory support, mortality rates in cardiogenic shock (CS) remain high. A reliable risk stratification system could serve as a valuable guide in the clinical management of patients with ...

Deep Learning-Enabled Assessment of Right Ventricular Function Improves Prognostication After Transcatheter Edge-to-Edge Repair for Mitral Regurgitation.

Circulation. Cardiovascular imaging
BACKGROUND: Right ventricular (RV) function has a well-established prognostic role in patients with severe mitral regurgitation (MR) undergoing transcatheter edge-to-edge repair (TEER) and is typically assessed using echocardiography-measured tricusp...

TKA-AID: An Uncertainty-Aware Deep Learning Classifier to Identify Total Knee Arthroplasty Implants.

The Journal of arthroplasty
BACKGROUND: A drastic increase in the volume of primary total knee arthroplasties (TKAs) performed nationwide will inevitably lead to higher volumes of revision TKAs in which the primary knee implant must be removed. An important step in preoperative...

Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.

Clinical and experimental nephrology
BACKGROUND: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinica...

Adverse Outcomes after Cemented and Cementless Primary Elective Total Hip Arthroplasty in 60,064 Matched Patients: A Study of Data from the Swedish Arthroplasty Register.

The Journal of arthroplasty
BACKGROUND: The choice between cemented and cementless fixation in primary elective total hip arthroplasty (THA) remains a subject of ongoing debate. However, comparisons between the two are subject to limited adjustments for patient characteristics,...

A deep learning approach versus expert clinician panel in the classification of posterior circulation infarction.

NeuroImage. Clinical
BACKGROUND: Posterior circulation infarction (POCI) is common. Imaging techniques such as non-contrast-CT (NCCT) and diffusion-weighted-magnetic-resonance-imaging commonly fail to detect hyperacute POCI. Studies suggest expert inspection of Computed ...

ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.

JCO clinical cancer informatics
PURPOSE: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based ...

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

PloS one
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 20...