Last Posted: Jan 24, 2023
- Leveraging Clinical Informatics and Data Science to Improve Care and Facilitate Research in Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference.
Sanchez-Pinto L Nelson et al. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 2023 24(Supplement 1 2S) S1-S11
- A Retrospective, Nested Case-Control Study to Develop a Biomarker-Based Model for ARDS Diagnostics.
Fu Xuan et al. Clinical laboratory 2023 69(1)
- Developing and evaluating a machine-learning-based algorithm to predict the incidence and severity of ARDS with continuous non-invasive parameters from ordinary monitors and ventilators.
Wu Wenzhu et al. Computer methods and programs in biomedicine 2023 230107328
- Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning.
Afshin-Pour Babak et al. Intelligence-based medicine 2023 100087
- Machine learning models for predicting acute kidney injury in patients with sepsis associated ARDS.
Zhou Yang et al. Shock (Augusta, Ga.) 2023
- Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium
B Moal et al, PLOS ONE, Jan 4, 2023
- Circulating proteome of hospitalized patients uncovers six endophenotypes of COVID-19 and points to FGFR and SHC4-signaling in acute respiratory distress syndrome
W Ma et al, MEDRXIV, November 9, 2022
- Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS
R Batra et al, MEDRXIV, August 13, 2022
- Drivers of Mortality in COVID ARDS Depend on Patient Sub-Type
H Cheyne et al, MEDRXIV, July 6,2022
- Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning.
Wu Junwei et al. BMC pulmonary medicine 2022 22(1) 193
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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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 the CDC Office of Public Health Genomics 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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