AIMC Topic: Predictive Value of Tests
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Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis.
Respiratory research
Jul 2, 2025
BACKGROUND: The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the urgency advanced predictive tools. This study aimed to develop a machine learning-based predictive ...
Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above.
BMC geriatrics
Jul 2, 2025
BACKGROUND: Non-ST segment elevation myocardial infarction (Non-STEMI) is a severe cardiovascular condition mainly affecting individuals aged 75 and above, who are at higher risk of mortality due to age-related vulnerabilities and other health issues...
Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.
Scientific reports
Jul 1, 2025
Some studies have developed machine learning (ML) models for the prediction of pneumonitis following immunotherapy and radiotherapy for patients with lung cancer (LC). However, the prediction accuracy of these models remains a topic of debate. Thus, ...
Machine learning-based comparison of transperineal vs. transrectal biopsy for prostate cancer diagnosis: evaluating procedural effectiveness.
The Canadian journal of urology
Jun 27, 2025
BACKGROUND: Transrectal (TR) and transperineal (TP) biopsies are commonly used methods for diagnosing prostate cancer. However, their comparative effectiveness in conjunction with machine learning (ML) techniques remains underexplored. This study aim...
Comprehensive predictive modeling in subarachnoid hemorrhage: integrating radiomics and clinical variables.
Neurosurgical review
Jun 24, 2025
Subarachnoid hemorrhage (SAH) is a severe condition with high morbidity and long-term neurological consequences. Radiomics, by extracting quantitative features from Computed Tomograhpy (CT) scans, may reveal imaging biomarkers predictive of outcomes....
Machine learning model for the prediction of postoperative acute kidney injury (AKI) in neonates undergoing digestive surgery.
Pediatric surgery international
Jun 24, 2025
OBJECTIVE: The aim of our study is to determine the main predictors of postoperative AKI in neonates using machine learning models compared with the logistic regression model.
Inflammation-related 5-hydroxymethylation signatures as markers for clinical presentations of coronary artery disease.
Cardiovascular diabetology
Jun 23, 2025
BACKGROUND: Coronary angiography remains the gold standard for diagnosing coronary artery disease (CAD), but its invasive nature limits its applicability for widespread screening. Identifying non-invasive molecular markers could improve CAD classific...
Predictive Value of Tests
ST Elevation Myocardial Infarction
Coronary Artery Disease
Inflammation Mediators
5-Methylcytosine
Inflammation
DNA Methylation
Cell-Free Nucleic Acids
Non-ST Elevated Myocardial Infarction
Prognosis
Female
Reproducibility of Results
Aged
Risk Assessment
Severity of Illness Index
Epigenesis, Genetic
Male
Middle Aged
Humans
Machine Learning
Biomarkers
Deep learning neural network prediction of postoperative complications in patients undergoing laparoscopic right hemicolectomy with or without CME and CVL for colon cancer: insights from SICE (Società Italiana di Chirurgia Endoscopica) CoDIG data.
Techniques in coloproctology
Jun 11, 2025
BACKGROUND: Postoperative complications in colorectal surgery can significantly impact patient outcomes and healthcare costs. Accurate prediction of these complications enables targeted perioperative management, improving patient safety and optimizin...
Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease.
Italian journal of pediatrics
Jun 9, 2025
BACKGROUND: Kawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10-20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. E...
Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.
BMC cardiovascular disorders
Jun 2, 2025
OBJECTIVE: Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (AF). This study aimed to evaluate the incidence and predictors of LAT in patients with paroxysmal AF...
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