AI Medical Compendium Journal:
ESC heart failure

Showing 11 to 20 of 35 articles

Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation.

ESC heart failure
AIMS: Assessing the risk for HF rehospitalization is important for managing and treating patients with HF. To address this need, various risk prediction models have been developed. However, none of them used deep learning methods with real-world data...

Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.

ESC heart failure
AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes....

The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support.

ESC heart failure
AIMS: Artificial intelligence (AI) has emerged as a potential useful tool to support clinical treatment of heart failure, including the setting of mechanical circulatory support (MCS). Modern Impella pumps are equipped with advanced technology (Smart...

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

ESC heart failure
AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emer...

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients.

ESC heart failure
AIMS: In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceiv...

Applying natural language processing to identify emergency department and observation encounters for worsening heart failure.

ESC heart failure
AIMS: Worsening heart failure (WHF) events occurring in non-inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)-based approach compared wit...

Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.

ESC heart failure
AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. ...

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.

ESC heart failure
AIMS: Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant thera...

Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.

ESC heart failure
Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (tra...

Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.

ESC heart failure
AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering th...