AIMC Topic: Cardiovascular Diseases

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LACE-UP: An ensemble machine-learning method for health subtype classification on multidimensional binary data.

Proceedings of the National Academy of Sciences of the United States of America
Disease and behavior subtype identification is of significant interest in biomedical research. However, in many settings, subtype discovery is limited by a lack of robust statistical clustering methods appropriate for binary data. Here, we introduce ...

FADE: Forecasting for anomaly detection on ECG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multip...

Explainable machine learning algorithm to predict cardiovascular event in patients undergoing peritoneal dialysis.

BMC medical informatics and decision making
OBJECTIVE: To compare the performance of predictive models for cardiovascular event (CVE) in patients undergoing peritoneal dialysis (PD) based on machine learning algorithm and Cox proportional hazard regression.

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

Cancer control : journal of the Moffitt Cancer Center
BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can lead to cardiovascular-related unplanned readmissions.ObjectiveWe aimed to develop a machine learnin...

Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model.

Scientific reports
Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimizati...

The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.

Depression and anxiety
The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. Machine learning (ML) algorithms were also selected aimed at pr...

Assessing the cardioprotective effects of exercise in APOE mouse models using deep learning and photon-counting micro-CT.

PloS one
BACKGROUND: The allelic variations of the apolipoprotein E (APOE) gene play a critical role in regulating lipid metabolism and significantly impact cardiovascular disease risk (CVD). This study aimed to evaluate the impact of exercise on cardiac stru...

The present and future of cardiological telemonitoring in Europe: a statement from seven European countries.

Herzschrittmachertherapie & Elektrophysiologie
Cardiovascular diseases remain one of the leading causes of death worldwide, placing a significant burden on individuals, families and healthcare systems. Telemedicine, in particular remote monitoring of patients with cardiovascular diseases, reduces...