Applications of artificial intelligence− machine learning for detection of stress: a critical overview
Psychological distress is a major contributor to human physiology and pathophysiology, and
it has been linked to several conditions, such as auto-immune diseases, metabolic …
it has been linked to several conditions, such as auto-immune diseases, metabolic …
Internet of things and artificial intelligence in healthcare during covid-19 pandemic—a south american perspective
P Chatterjee, A Tesis, LJ Cymberknop… - Frontiers in Public …, 2020 - frontiersin.org
The shudders of the COVID-19 pandemic have projected newer challenges in the
healthcare domain across the world. In South American scenario, severe issues and …
healthcare domain across the world. In South American scenario, severe issues and …
[HTML][HTML] Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning
Objective Open surgical treatment options for aortoiliac occlusive disease carry significant
perioperative risks; however, outcome prediction tools remain limited. Using machine …
perioperative risks; however, outcome prediction tools remain limited. Using machine …
Predicting outcomes following endovascular abdominal aortic aneurysm repair using machine learning
Objective: To develop machine learning (ML) models that predict outcomes following
endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). Background …
endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). Background …
AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations
Background Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is
linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications …
linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications …
Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting
Background Transfemoral carotid artery stenting (TFCAS) carries important perioperative
risks. Outcome prediction tools may help guide clinical decision‐making but remain limited …
risks. Outcome prediction tools may help guide clinical decision‐making but remain limited …
A machine learning algorithm for peripheral artery disease prognosis using biomarker data
Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an
algorithm that considers a protein panel to inform PAD prognosis may improve predictive …
algorithm that considers a protein panel to inform PAD prognosis may improve predictive …
Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease
Objective: To develop machine learning (ML) algorithms that predict outcomes after
infrainguinal bypass. Background: Infrainguinal bypass for peripheral artery disease carries …
infrainguinal bypass. Background: Infrainguinal bypass for peripheral artery disease carries …
[HTML][HTML] Predicting inferior vena cava filter complications using machine learning
Objective Inferior vena cava (IVC) filter placement is associated with important long-term
complications. Predictive models for filter-related complications may help guide clinical …
complications. Predictive models for filter-related complications may help guide clinical …
Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD
Importance Endovascular intervention for peripheral artery disease (PAD) carries
nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective …
nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective …