Last Posted: Jun 23, 2023
- Optimized Risk Score to Predict Mortality in Patients With Cardiogenic Shock in the Cardiac Intensive Care Unit.
Eric Yamga et al. J Am Heart Assoc 2023 e029232
- Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review.
Mohammad A Al-Ani et al. Frontiers in cardiovascular medicine 2023 101127716
- Diagnostic Model of In-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods.
Li Yong et al. Cardiology research and practice 2022 20228758617
- Clinical-Epidemiological Characteristics and Mortality in Patients with Sickle Cell Anemia: A Retrospective Cohort Study of 1980 at 2018.
Pompeo Carolina Mariano et al. International journal of general medicine 2022 151057-1074
- Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.
Jentzer Jacob C et al. Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions 2022
- Machine learning prediction model of acute kidney injury after percutaneous coronary intervention.
Kuno Toshiki et al. Scientific reports 2022 12(1) 749
- Contemporary Clinical and Coronary Anatomic Risk Model for 30-Day Mortality After Percutaneous Coronary Intervention.
Doll Jacob A et al. Circulation. Cardiovascular interventions 2021 CIRCINTERVENTIONS121010863
- Cardiogenic shock and machine learning: A systematic review on prediction through clinical decision support softwares.
Aleman Rene et al. Journal of cardiac surgery 2021
- Integrating the STOP-BANG score and clinical data to predict cardiovascular events after infarction: A machine learning study.
Calvillo-Argüelles Oscar et al. Chest 2020 Apr
- Dilated Cardiomyopathy - From Epidemiologic to Genetic Phenotypes A Translational Review of Current Literature.
Reichart Daniel et al. Journal of internal medicine 2019 May
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
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.
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