AIMC Topic: Heart Failure
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Fairness gaps in Machine learning models for hospitalization and emergency department visit risk prediction in home healthcare patients with heart failure.
International journal of medical informatics
Jun 30, 2024
OBJECTIVES: This study aims to evaluate the fairness performance metrics of Machine Learning (ML) models to predict hospitalization and emergency department (ED) visits in heart failure patients receiving home healthcare. We analyze biases, assess pe...
Pulse wave signal-driven machine learning for identifying left ventricular enlargement in heart failure patients.
Biomedical engineering online
Jun 22, 2024
BACKGROUND: Left ventricular enlargement (LVE) is a common manifestation of cardiac remodeling that is closely associated with cardiac dysfunction, heart failure (HF), and arrhythmias. This study aimed to propose a machine learning (ML)-based strateg...
Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record.
ESC heart failure
Jun 14, 2024
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....
Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.
Journal of biomedical informatics
Jun 13, 2024
OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory c...
Point-of-care AI-enhanced novice echocardiography for screening heart failure (PANES-HF).
Scientific reports
Jun 12, 2024
The increasing prevalence of heart failure (HF) in ageing populations drives demand for echocardiography (echo). There is a worldwide shortage of trained sonographers and long waiting times for expert echo. We hypothesised that artificial intelligenc...
Comment on "Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients' questions about heart failure: Doi: 10.1016/j.ijcard.2024.132115".
International journal of cardiology
Jun 5, 2024
Pleiotropic Effects of Direct Oral Anticoagulants in Chronic Heart Failure and Atrial Fibrillation: Machine Learning Analysis.
Molecules (Basel, Switzerland)
Jun 4, 2024
Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mainstay of anticoagulation therapy before 2010, and direct-acting oral anticoagulants (DOACs, namely dab...
Classification tree obtained by artificial intelligence for the prediction of heart failure after acute coronary syndromes.
Medicina clinica
May 30, 2024
BACKGROUND: Coronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of developing HF after an acute coronary syndrome (ACS). Artificial intelligence (AI) has proven to be ...
A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure.
Internal and emergency medicine
May 23, 2024
Lung ultrasound (LUS) is an effective tool for diagnosing acute heart failure (AHF). However, several imaging protocols currently exist and how to best use LUS remains undefined. We aimed at developing a lung ultrasound-based model for AHF diagnosis ...
The intelligent Impella: Future perspectives of artificial intelligence in the setting of Impella support.
ESC heart failure
May 23, 2024
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...
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