Heart Failure
What's New
Last Posted: Jun 19, 2024
- Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.
Yifei Wana et al. J Biomed Inform 2024 104677 - Subspecialty Health Care Utilization in Pediatric Patients With Muscular Dystrophy in the United States.
Susan E Matesanz et al. Neurol Clin Pract 2024 14(4) e200312 - A machine learning approach to classifying New York Heart Association (NYHA) heart failure.
Krystian Jandy et al. Sci Rep 2024 14(1) 11496 - Predictors of 30-day readmission based on machine learning in patients with heart failure: an essential assessment for precision care.
Bei Dou et al. Eur J Cardiovasc Nurs 2024 - Addressing Health Disparities—The Case for Variant Transthyretin Cardiac Amyloidosis Grows Stronger
- Predictors of Disease Progression and Adverse Clinical Outcomes in Patients with Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.
Mahmoud Salem et al. Am J Cardiol 2024 - Accuracy and consistency of online large language model-based artificial intelligence chat platforms in answering patients' questions about heart failure.
Elie Kozaily et al. Int J Cardiol 2024 132115 - Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.
Zhenyue Gao et al. J Med Internet Res 2024 26e54363 - Carpal Tunnel Syndrome and Transthyretin Amyloidosis in the All of Us Research Program.
Naman S Shetty et al. Mayo Clin Proc 2024 - Heart failure survival prediction using novel transfer learning based probabilistic features.
Azam Mehmood Qadri et al. PeerJ Comput Sci 2024 10e1894 - Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms.
Odeh Adeyi Victor et al. Sensors (Basel) 2024 24(7) - Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study.
Richard M Yoo et al. JMIR Med Inform 2024 12e51171 - Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol.
Joana Seringa et al. BMJ Open 2024 14(4) e083188 - Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.
Faraz S Ahmad et al. Clin Res Cardiol 2024 - Application of machine learning in predicting frailty syndrome in patients with heart failure.
Remigiusz Szczepanowski et al. Adv Clin Exp Med 2024 33(3) 309-315 - Genetic Characterization of Dilated Cardiomyopathy in Romanian Adult Patients.
Oana Raluca Voinescu et al. Int J Mol Sci 2024 25(5) - An extra X chromosome among adult women in the Million Veteran Program: A more benign perspective of trisomy X.
Shanlee M Davis et al. Am J Med Genet C Semin Med Genet 2024 e32083 - Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data.
Jun-Bo Tu et al. Sci Rep 2024 14(1) 5245 - Cardiomyopathies in children and adolescents: aetiology, management, and outcomes in the European Society of Cardiology EURObservational Research Programme Cardiomyopathy and Myocarditis Registry.
Juan Pablo Kaski et al. Eur Heart J 2024 - Cloud-Based Machine Learning Platform to Predict Clinical Outcomes at Home for Patients With Cardiovascular Conditions Discharged From Hospital: Clinical Trial.
Phillip C Yang et al. JMIR Cardio 2024 8e45130
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About HLBS-PopOmics
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Site Citation:
Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
- Page last updated:Jun 21, 2024
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