AI Medical Compendium Topic

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Statistics, Nonparametric

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The role of klotho in systemic sclerosis.

Reumatismo
The aim was to evaluate the role of klotho in the pathogenesis of systemic sclerosis (SSc), through the measurement of its serum concentration in SSc patients compared to healthy controls, and to assess the association with cutaneous and visceral inv...

A Predictive Model for Guillain-Barré Syndrome Based on Ensemble Methods.

Computational intelligence and neuroscience
Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain-Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has...

Predictive pollen-based biome modeling using machine learning.

PloS one
This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published Af...

Relative wave energy-based adaptive neuro-fuzzy inference system for estimation of the depth of anaesthesia.

Journal of integrative neuroscience
The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent model...

Randomized controlled trial of Amigo® robotically controlled versus manually controlled ablation of the cavo-tricuspid isthmus using a contact force ablation catheter.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
BACKGROUND: Radiofrequency catheter ablation (RFCA) of the cavo-tricuspid isthmus (CTI) is a common treatment for atrial flutter (AFL). However, achieving bi-directional CTI conduction block may be difficult, partly due to catheter instability.

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

Journal of psychiatric research
Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well es...

Disrupted functional connectivity within the default mode network and salience network in unmedicated bipolar II disorder.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Recent studies demonstrate that functional disruption in resting-state networks contributes to cognitive and affective symptoms of bipolar disorder (BD), however, the functional connectivity (FC) pattern underlying BD II depression within...

Development and Application of a System Based on Artificial Intelligence for Transcatheter Aortic Prosthesis Selection.

Brazilian journal of cardiovascular surgery
INTRODUCTION: The interest in Expert systems has increased in the medical area. Some of them are employed even for diagnosis. With the variability of transcatheter prostheses, the most appropriate choice can be complex. This scenario reveals an enabl...

Identification of Novel Genes in Human Airway Epithelial Cells associated with Chronic Obstructive Pulmonary Disease (COPD) using Machine-Based Learning Algorithms.

Scientific reports
The aim of this project was to identify candidate novel therapeutic targets to facilitate the treatment of COPD using machine-based learning (ML) algorithms and penalized regression models. In this study, 59 healthy smokers, 53 healthy non-smokers an...