How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management
There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …
[HTML][HTML] Comparing the performance of published risk scores in Brugada syndrome: a multi-center cohort study
Abstract The management of Brugada Syndrome (BrS) patients at intermediate risk of
arrhythmic events remains controversial. The present study evaluated the predictive …
arrhythmic events remains controversial. The present study evaluated the predictive …
The neutrophil-to-lymphocyte ratio is an important indicator predicting in-hospital death in AMI patients
Z Ji, G Liu, J Guo, R Zhang, Y Su, A Carvalho… - Frontiers in …, 2021 - frontiersin.org
Objective: To explore the role of neutrophil-to-lymphocyte ratio (NLR) in predicting the short-
term prognosis of NSTEMI and STEMI. Methods: This study was a single-center …
term prognosis of NSTEMI and STEMI. Methods: This study was a single-center …
A multimodal deep learning-based fault detection model for a plastic injection molding process
The authors of this work propose a deep learning-based fault detection model that can be
implemented in the field of plastic injection molding. Compared to conventional approaches …
implemented in the field of plastic injection molding. Compared to conventional approaches …
Supervised machine learning for the assessment of chronic kidney disease advancement
P Ventrella, G Delgrossi, G Ferrario, M Righetti… - Computer methods and …, 2021 - Elsevier
ABSTRACT Background and objective: Chronic Kidney Disease (CKD) is a condition
characterized by a progressive loss of kidney function over time caused by many diseases …
characterized by a progressive loss of kidney function over time caused by many diseases …
Derivation of an electronic frailty index for predicting short‐term mortality in heart failure: a machine learning approach
Aims Frailty may be found in heart failure patients especially in the elderly and is associated
with a poor prognosis. However, assessment of frailty status is time‐consuming, and the …
with a poor prognosis. However, assessment of frailty status is time‐consuming, and the …
[HTML][HTML] Healthcare Big Data in Hong Kong: development and implementation of artificial intelligence-enhanced predictive models for risk stratification
Routinely collected electronic health records (EHRs) data contain a vast amount of valuable
information for conducting epidemiological studies. With the right tools, we can gain insights …
information for conducting epidemiological studies. With the right tools, we can gain insights …
[HTML][HTML] Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis
LI Xin-Mu, GAO Xin-Yi, G Tse, H Shen-Da… - Journal of geriatric …, 2022 - ncbi.nlm.nih.gov
BACKGROUND The electrocardiogram (ECG) is an inexpensive and easily accessible
investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The …
investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The …
Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data
A Radhachandran, A Garikipati, NS Zelin, E Pellegrini… - BioData mining, 2021 - Springer
Background Acute heart failure (AHF) is associated with significant morbidity and mortality.
Effective patient risk stratification is essential to guiding hospitalization decisions and the …
Effective patient risk stratification is essential to guiding hospitalization decisions and the …
Development and validation of echocardiography-based machine-learning models to predict mortality
Background Echocardiography (echo) based machine learning (ML) models may be useful
in identifying patients at high-risk of all-cause mortality. Methods We developed ML models …
in identifying patients at high-risk of all-cause mortality. Methods We developed ML models …