AIMC Topic: Risk Factors

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Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study.

Morphologie : bulletin de l'Association des anatomistes
Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understandi...

Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology.

Scandinavian journal of urology
To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality. Material consi...

Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques.

Preventing chronic disease
INTRODUCTION: As one of the most prevalent chronic diseases in the United States, diabetes, especially type 2 diabetes, affects the health of millions of people and puts an enormous financial burden on the US economy. We aimed to develop predictive m...

Development and verification of prediction models for preventing cardiovascular diseases.

PloS one
OBJECTIVES: Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to a...

Initial report of safety and procedure duration of robotic-assisted chronic total occlusion coronary intervention.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions
BACKGROUND: No previous reports have examined the impact of robotic-assisted (RA) chronic total occlusion (CTO) PCI on procedural duration or safety compared to totally manual CTO PCI.

Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study.

Scientific reports
A comprehensive screening method using machine learning and many factors (biological characteristics, Helicobacter pylori infection status, endoscopic findings and blood test results), accumulated daily as data in hospitals, could improve the accurac...