AIMC Topic: Risk Factors

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Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review.

Computer methods and programs in biomedicine
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. I...

Different spexin level in obese vs normal weight children and its relationship with obesity related risk factors.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: Spexin (SPX) is a novel peptide recently discovered as an important regulatory adipokine in obesity and related metabolic diseases. The aim of the current study was to determine the potential role of Circulating levels of SPX in ...

Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects.

Risk analysis : an official publication of the Society for Risk Analysis
Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the a...

Prediction of lithium response using clinical data.

Acta psychiatrica Scandinavica
OBJECTIVE: Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical ma...

A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases.

Artificial intelligence in medicine
Chronic diseases often cause several medical complications. This paper aims to predict multiple complications among patients with a chronic disease. The literature uses single-task learning algorithms to predict complications independently and assume...

Predictive Utility of a Machine Learning Algorithm in Estimating Mortality Risk in Cardiac Surgery.

The Annals of thoracic surgery
BACKGROUND: This study evaluated the predictive utility of a machine learning algorithm in estimating operative mortality risk in cardiac surgery.

Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning.

Geospatial health
Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of d...

Statistical and machine learning methodology for abdominal aortic aneurysm prediction from ultrasound screenings.

Echocardiography (Mount Kisco, N.Y.)
A method of analysis of a database of patients (n = 10 329) screened for an abdominal aortic aneurysm (AAA) is presented. Self-reported height, weight, age, gender, ethnicity, and parameters "Heart Problems," "Hypertension," "High Cholesterol," "Diab...

Predicting atrial fibrillation in primary care using machine learning.

PloS one
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of t...