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Heart Failure

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Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

Journal of cardiothoracic and vascular anesthesia
BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict...

Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study.

JMIR medical informatics
BACKGROUND: Patients with heart failure frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and health care systems. Accurate predictive tools are crucial for guiding ...

Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study.

Journal of medical Internet research
BACKGROUND: Heart failure (HF) is a significant global health problem, affecting approximately 64.34 million people worldwide. The worsening of HF, also known as HF decompensation, is a major factor behind hospitalizations, contributing to substantia...

Survival parametric modeling for patients with heart failure based on Kernel learning.

BMC medical research methodology
Time-to-event data are very common in medical applications. Regression models have been developed on such data especially in the field of survival analysis. Kernels are used to handle even more complicated and enormous quantities of medical data by i...

Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment.

Physiological reports
"I see, I forget, I read aloud, I remember, and when I do read purposefully by writing it, I do not forget it." This phenomenon is known as "interoception" and refers to the sensing and interpretation of internal body signals, allowing the brain to c...

Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.

The international journal of cardiovascular imaging
Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessme...

Deep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.

European heart journal. Acute cardiovascular care
AIMS: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep learning...

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.

European heart journal
BACKGROUND AND AIMS: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF r...