AIMC Topic: Risk Factors

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Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.

BMC cardiovascular disorders
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...

Recurrent pregnancy loss: risk factors and predictive modeling approaches.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians
PURPOSE: This review aims to identify and analyze the risk factors associated with recurrent pregnancy loss (RPL) and to evaluate the effectiveness of various predictive models in estimating the risk of RPL. The review also explores recent advancemen...

Textbook outcome in liver surgery for intrahepatic cholangiocarcinoma: defining predictors of an optimal postoperative course using machine learning.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: We sought to define textbook outcome in liver surgery (TOLS) for intrahepatic cholangiocarcinoma (ICC) by considering the implications of perioperative outcomes on overall survival (OS).

Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.

Atherosclerosis
BACKGROUND AND AIMS: An in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, cor...

Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning.

European journal of heart failure
AIMS: Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), leading to increased symptom burden and risk of thromboembolism. The HCM-AF score was developed to predict new-onset AF in p...

Clinical characteristics and prediction model of re-positive nucleic acid tests among Omicron infections by machine learning: a real-world study of 35,488 cases.

BMC infectious diseases
BACKGROUND: During the Omicron BA.2 variant outbreak in Shanghai, China, from April to May 2022, PCR nucleic acid test re-positivity (TR) occurred frequently, yet the risk factors and predictive models for TR remain unclear. This study aims to identi...

Machine Learning-Based Model for Prediction of Post-Stroke Cognitive Impairment in Acute Ischemic Stroke: A Cross-Sectional Study.

Neurology India
BACKGROUND AND OBJECTIVE: Early identification of post-stroke cognitive impairment (PSCI) is an important challenge for clinicians. In this study, we aimed to build a machine learning-based prediction model for PSCI and uncover potential risk factors...