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Heart Failure

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Focus on emerging cancer drugs, artificial intelligence applied to imaging, and a new therapeutic target in heart failure.

European heart journal Jun 7, 2024
Humans Artificial Intelligence Neoplasms Antineoplastic Agents Heart Failure
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Deep learning to detect left ventricular structural abnormalities in chest X-rays.

European heart journal Jun 7, 2024
BACKGROUND AND AIMS: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportuni...
Male Echocardiography Middle Aged Female Aged Heart Failure Radiography, Thoracic Humans Hypertrophy, Left Ventricular Deep Learning ROC Curve Heart Ventricles
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Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.

Clinical cardiology May 1, 2024
Humans Female Risk Factors Aged Machine Learning Prognosis Retrospective Studies Risk Assessment Heart Failure Patient Readmission Male Middle Aged
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Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.

Journal of the American Medical Informatics Association : JAMIA Apr 3, 2024
OBJECTIVES: We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (...
Ambulatory Care Facilities Information Technology Communication Heart Failure Humans Artificial Intelligence
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Development of deep-learning models for real-time anaerobic threshold and peak VO2 prediction during cardiopulmonary exercise testing.

European journal of preventive cardiology Mar 4, 2024
AIMS: Exercise intolerance is a clinical feature of patients with heart failure (HF). Cardiopulmonary exercise testing (CPET) is the first-line examination for assessing exercise capacity in patients with HF. However, the need for extensive experienc...
Exercise Test Heart Failure Anaerobic Threshold Humans Deep Learning Oxygen Consumption
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Utilizing machine learning for predicting heart failure outcomes: A path toward developing a patient-centered approach.

Clinical cardiology Mar 1, 2024
Patient-Centered Care Heart Failure Humans Machine Learning
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Interpretable Machine Learning Approach for Predicting 30-Day Mortality of Critical Ill Patients with Pulmonary Embolism and Heart Failure: A Retrospective Study.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis Jan 1, 2024
BACKGROUND: Pulmonary embolism (PE) patients combined with heart failure (HF) have been reported to have a high short-term mortality. However, few studies have developed predictive tools of 30-day mortality for these patients in intensive care unit (...
Pulmonary Embolism Humans Heart Failure Retrospective Studies Machine Learning Aged Critical Illness Male Female Middle Aged
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Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis Jan 1, 2024
BACKGROUND: Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions...
Male Middle Aged Acute Disease Heart Failure Patient Readmission Humans Retrospective Studies Machine Learning Prognosis Female Aged
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Applications of deep learning models in precision prediction of survival rates for heart failure patients.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: Heart failure poses a significant challenge in the global health domain, and accurate prediction of mortality is crucial for devising effective treatment plans. In this study, we employed a Seq2Seq model from deep learning, integrating 12...
Male Middle Aged Deep Learning Humans Heart Failure Machine Learning Female Risk Factors Aged Survival Rate
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Using artificial intelligence-enabled electrocardiogram to predict cardiac resynchronization therapy outcomes of left bundle branch area pacing.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Dec 28, 2023
Heart Failure Ventricular Function, Left Humans Cardiac Resynchronization Therapy Electrocardiography Heart Conduction System Treatment Outcome Artificial Intelligence Bundle of His Cardiac Pacing, Artificial
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