AIMC Topic: Decision Support Techniques

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Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes - A large multi-centre study in Australia and New Zealand.

Nutrition, metabolism, and cardiovascular diseases : NMCD Jan 9, 2025
BACKGROUND AND AIMS: Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.
Prognosis Support Vector Machine Humans Female Australia Predictive Value of Tests Machine Learning Risk Factors Glycated Hemoglobin Decision Support Techniques Young Adult New Zealand Prospective Studies Adult Biomarkers Male Middle Aged Risk Assessment Deep Learning Adolescent Diabetes Mellitus, Type 1 Diabetic Ketoacidosis
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Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model.

Transplantation Jan 9, 2025
BACKGROUND: Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered usin...
Machine Learning Algorithms Decision Support Techniques Retrospective Studies Female Risk Factors Humans Tissue Donors Graft Survival Treatment Outcome Predictive Value of Tests Aged Liver Transplantation Perfusion Adult Spain Male Middle Aged Risk Assessment Time Factors
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Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.

The international journal of cardiovascular imaging Jan 9, 2025
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessme...
Radiography, Thoracic Deep Learning Time Factors Hospital Mortality Predictive Value of Tests Feasibility Studies Heart Failure Risk Factors Aged Aged, 80 and over Male Middle Aged Risk Assessment Humans Decision Support Techniques Prognosis Electronic Health Records Female Reproducibility of Results
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Predicting 28-day all-cause mortality in patients admitted to intensive care units with pre-existing chronic heart failure using the stress hyperglycemia ratio: a machine learning-driven retrospective cohort analysis.

Cardiovascular diabetology Jan 8, 2025
Chronic heart failure (CHF) poses a significant threat to human health. The stress hyperglycemia ratio (SHR) is a novel metric for accurately assessing stress hyperglycemia, which has been correlated with adverse outcomes in various major diseases. H...
Hyperglycemia Chronic Disease Predictive Value of Tests Cause of Death Heart Failure Patient Admission Aged, 80 and over Male Middle Aged Risk Assessment Blood Glucose Time Factors Humans Machine Learning Decision Support Techniques Databases, Factual Biomarkers Prognosis Retrospective Studies Intensive Care Units Female Risk Factors Aged
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Deep-learning prediction of cardiovascular outcomes from routine retinal images in individuals with type 2 diabetes.

Cardiovascular diabetology Jan 2, 2025
BACKGROUND: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learnin...
Risk Factors Aged Male Cardiovascular Diseases Middle Aged Risk Assessment Deep Learning Humans Diabetic Retinopathy Decision Support Techniques Prognosis Female Reproducibility of Results Retinal Vessels Time Factors Diabetes Mellitus, Type 2 Predictive Value of Tests Heart Disease Risk Factors Image Interpretation, Computer-Assisted Photography
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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model.

Stroke and vascular neurology Dec 30, 2024
BACKGROUND: Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning met...
Humans Risk Assessment Time Factors Treatment Outcome Predictive Value of Tests Ischemic Stroke Reproducibility of Results Endovascular Procedures Risk Factors Medical Futility Machine Learning Aged Decision Support Techniques Aged, 80 and over Databases, Factual Male Prospective Studies Middle Aged Clinical Decision-Making Female
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Predicting lack of clinical improvement following varicose vein ablation using machine learning.

Journal of vascular surgery. Venous and lymphatic disorders Dec 26, 2024
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...
Female Postoperative Complications Humans Time Factors Risk Factors Machine Learning Treatment Outcome Aged Decision Support Techniques Predictive Value of Tests Male Databases, Factual Ablation Techniques Middle Aged Retrospective Studies Varicose Veins Risk Assessment Clinical Decision-Making
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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 Dec 24, 2024
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...
Predictive Value of Tests Humans Coronary Artery Bypass Machine Learning Intra-Aortic Balloon Pumping Aged Decision Support Techniques Male Retrospective Studies Middle Aged Intensive Care Units Risk Assessment Female Length of Stay Reproducibility of Results Time Factors Risk Factors Treatment Outcome
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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 Dec 24, 2024
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...
Ischemic Stroke Sleep Apnea, Obstructive Sleep Polysomnography Aged Humans Male Artificial Intelligence Middle Aged Machine Learning Risk Assessment Decision Support Techniques Severity of Illness Index Female Predictive Value of Tests Risk Factors
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Machine Learning-Driven Identification of Distinct Persistent Atrial Fibrillation Phenotypes: A Cluster Analysis of DECAAF II.

Journal of cardiovascular electrophysiology Dec 23, 2024
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...
Middle Aged Risk Assessment Time Factors Treatment Outcome Cluster Analysis Action Potentials Predictive Value of Tests Atrial Fibrillation Heart Rate Catheter Ablation Recurrence Phenotype Female Reproducibility of Results Risk Factors Humans Aged Machine Learning Male Decision Support Techniques
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