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Decision Support Techniques

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Predicting lack of clinical improvement following varicose vein ablation using machine learning.

Journal of vascular surgery. Venous and lymphatic disorders 39732288
OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcers. However, patient selection for intervention in individuals without venous ulcers is less clear. Tools that predict lack of clinical improvement (LC...
Ablation Techniques Aged Clinical Decision-Making Databases, Factual Decision Support Techniques Female Humans Machine Learning Male Middle Aged Postoperative Complications Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Time Factors Treatment Outcome Varicose Veins
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Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 39722420
BACKGROUND: Obstructive sleep apnea (OSA) is common after stroke. Still, routine screening of OSA with polysomnography (PSG) is often unfeasible in clinical practice, primarily because of how limited resources are and the physical condition of patien...
Aged Artificial Intelligence Decision Support Techniques Female Humans Ischemic Stroke Machine Learning Male Middle Aged Polysomnography Predictive Value of Tests Risk Assessment Risk Factors Severity of Illness Index Sleep Sleep Apnea, Obstructive
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Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery.

Journal of cardiovascular translational research 39718687
This study aimed to construct machine learning models and predict prolonged intensive care units (ICU) stay in patients receiving perioperative intra-aortic balloon pump (IABP) therapy during cardiac surgery. 236 patients were divided into the normal...
Aged Coronary Artery Bypass Decision Support Techniques Female Humans Intensive Care Units Intra-Aortic Balloon Pumping Length of Stay Machine Learning Male Middle Aged Predictive Value of Tests Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors Time Factors Treatment Outcome
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Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

Journal of cardiovascular electrophysiology 39716785
INTRODUCTION: Catheter ablation of persistent atrial fibrillation yields sub-optimal success rates partly due to the considerable heterogeneity within the patient population. Identifying distinct patient phenotypes based on post-ablation prognosis co...
Action Potentials Aged Atrial Fibrillation Catheter Ablation Cluster Analysis Decision Support Techniques Female Heart Rate Humans Machine Learning Male Middle Aged Phenotype Predictive Value of Tests Recurrence Reproducibility of Results Risk Assessment Risk Factors Time Factors Treatment Outcome
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Using artificial intelligence to predict post-operative outcomes in congenital heart surgeries: a systematic review.

BMC cardiovascular disorders 39702050
INTRODUCTION: Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools...
Adolescent Adult Artificial Intelligence Cardiac Surgical Procedures Child Child, Preschool Decision Support Techniques Female Heart Defects, Congenital Humans Infant Infant, Newborn Male Postoperative Complications Predictive Value of Tests Risk Assessment Risk Factors Time Factors Treatment Outcome Young Adult
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Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.

BMC cardiovascular disorders 39702028
BACKGROUND: Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of catheter ablation is crucial for patient selection and management. This research seeks to create a machi...
Adult Aged Atrial Fibrillation Catheter Ablation Decision Support Techniques Female Humans Iran Longitudinal Studies Machine Learning Male Middle Aged Predictive Value of Tests Prospective Studies Recurrence Registries Risk Assessment Risk Factors Time Factors Treatment Outcome
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The TabNet Model for Diagnosing Axial Spondyloarthritis Using MRI Imaging Findings and Clinical Risk Factors.

International journal of rheumatic diseases 39690496
OBJECTIVES: The aim of this study is to develop and validate a model for predicting axial spondyloarthritis (axSpA) based on sacroiliac joint (SIJ)-MRI imaging findings and clinical risk factors.
Adult Axial Spondyloarthritis Decision Support Techniques Female Humans Image Interpretation, Computer-Assisted Machine Learning Magnetic Resonance Imaging Male Middle Aged Neural Networks, Computer Predictive Value of Tests Reproducibility of Results Risk Factors Sacroiliac Joint
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Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 39674434
BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP in stroke patients, leveraging national registry data and SHapley Add...
Aged Aged, 80 and over Decision Support Techniques Female Healthcare-Associated Pneumonia Hemorrhagic Stroke Humans Ischemic Stroke Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Registries Risk Assessment Risk Factors Stroke
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Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Trends in cardiovascular medicine 39653093
Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing signif...
Artificial Intelligence Atrial Fibrillation Decision Support Techniques Humans Machine Learning Precision Medicine Predictive Value of Tests Recovery of Function Risk Assessment Risk Factors Stroke Stroke Rehabilitation Treatment Outcome
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Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance.

JACC. Cardiovascular imaging 39641685
BACKGROUND: Cardiac magnetic resonance (CMR) imaging is an important diagnostic tool for diagnosis of cardiac amyloidosis (CA). However, discrimination of CA from other etiologies of myocardial disease can be challenging.
Aged Amyloidosis Cardiomyopathies Cardiomyopathy, Hypertrophic Contrast Media Decision Support Techniques Deep Learning Diagnosis, Differential Female Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging, Cine Male Middle Aged Myocardium Predictive Value of Tests Reproducibility of Results Retrospective Studies
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