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Predictive Value of Tests

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Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective.

BMC cardiovascular disorders 39979842
Alignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to ach...
Cardiovascular Diseases Decision Support Techniques Decision Trees Diagnosis, Computer-Assisted Humans Machine Learning Predictive Value of Tests Reproducibility of Results
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Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

Cardiovascular diabetology 39955587
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...
Aged Biomarkers Blood Glucose Cardiac Surgical Procedures Cause of Death Databases, Factual Decision Support Techniques Female Hospital Mortality Humans Hyperglycemia Machine Learning Male Middle Aged Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Stress, Physiological Time Factors Treatment Outcome
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Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

Cardiovascular diabetology 39955553
BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.
Aged Databases, Factual Decision Support Techniques Diabetes Mellitus Female Heart Disease Risk Factors Humans Incidence Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Renal Insufficiency, Chronic Risk Assessment Time Factors
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Preoperatively predicting failure to achieve the minimum clinically important difference and the substantial clinical benefit in patient-reported outcome measures for total hip arthroplasty patients using machine learning.

BMC musculoskeletal disorders 39953514
BACKGROUND: Attention to the collection of patient-reported outcomes measures (PROMs) associated with total hip arthroplasty (THA) is growing. The aim of this study was to preoperatively predict failure to achieve the minimal clinically important dif...
Aged Arthroplasty, Replacement, Hip Female Humans Machine Learning Male Middle Aged Minimal Clinically Important Difference Osteoarthritis, Hip Patient Reported Outcome Measures Predictive Value of Tests Retrospective Studies Treatment Failure
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Functional MRI-based machine learning strategy for prediction of postoperative delirium in cardiac surgery patients: A secondary analysis of a prospective observational study.

Journal of clinical anesthesia 39951939
STUDY OBJECTIVE: Delirium is a common complication after cardiac surgery and is associated with poor prognosis. An effective delirium prediction model could identify high-risk patients who might benefit from targeted prevention strategies. We introdu...
Aged Brain Cardiac Surgical Procedures China Delirium Emergence Delirium Female Heart Valve Prosthesis Implantation Humans Machine Learning Magnetic Resonance Imaging Male Middle Aged Postoperative Complications Predictive Value of Tests Prospective Studies
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A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study.

World journal of emergency surgery : WJES 39948568
BACKGROUND: Early treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in pati...
Adult Aged China Female Hospitalization Humans Machine Learning Male Middle Aged Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors ROC Curve Thoracic Injuries Venous Thromboembolism
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Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer 39945884
PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom burden clusters in breast cancer patients undergoing chemotherapy, and to determine an optimal model.
Adult Aged Algorithms Antineoplastic Agents Breast Neoplasms Decision Trees Female Humans Logistic Models Machine Learning Middle Aged Neural Networks, Computer Predictive Value of Tests ROC Curve Sensitivity and Specificity Support Vector Machine Symptom Burden
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Machine learning model using immune indicators to predict outcomes in early liver cancer.

World journal of gastroenterology 39926221
BACKGROUND: Patients with early-stage hepatocellular carcinoma (HCC) generally have good survival rates following surgical resection. However, a subset of these patients experience recurrence within five years post-surgery.
Aged Biomarkers, Tumor Carcinoma, Hepatocellular Decision Trees Female Hepatectomy Humans Liver Neoplasms Machine Learning Male Middle Aged Neoplasm Recurrence, Local Neoplasm Staging Neural Networks, Computer Predictive Value of Tests Prognosis Risk Assessment Risk Factors Survival Rate
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Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

Circulation. Arrhythmia and electrophysiology 39925268
BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia ...
Action Potentials Aged Atrial Fibrillation Catheter Ablation Electrocardiography Electrophysiologic Techniques, Cardiac Female Heart Rate Humans Machine Learning Male Middle Aged Phenotype Predictive Value of Tests Registries Time Factors Treatment Outcome
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External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 39960451
AIMS: The aim of this study was to perform an external validation of an automatic machine learning (ML) algorithm for heart rhythm diagnostics using smartphone photoplethysmography (PPG) recorded by patients with atrial fibrillation (AF) and atrial f...
Aged Algorithms Atrial Fibrillation Atrial Flutter Classification Algorithms Electric Countershock Electrocardiography Electrocardiography, Ambulatory Female Heart Rate Humans Machine Learning Male Middle Aged Mobile Applications Photoplethysmography Predictive Value of Tests Reproducibility of Results Signal Processing, Computer-Assisted Smartphone Support Vector Machine
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