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Last Posted: Mar 13, 2024
- 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 - Genetics of chronic respiratory disease.
Ian Sayers et al. Nat Rev Genet 2024 3 - Predicting early-onset COPD risk in adults aged 20-50 using electronic health records and machine learning.
Guanglei Liu et al. PeerJ 2024 12e16950 - Detection of Alpha-1 Antitrypsin Levels in Chronic Obstructive Pulmonary Disease in Respiratory Clinics in Spain: Results of the EPOCONSUL 2021 Audit.
Myriam Calle Rubio et al. J Clin Med 2024 13(4) - A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients.
Huiming Yin et al. Comput Methods Programs Biomed 2024 246108005 - Assessing a Polygenic Risk Score for Lung Cancer Susceptibility in Non-Hispanic White and Black Populations.
Matthew R Trendowski et al. Cancer Epidemiol Biomarkers Prev 2023 8 - Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease.
César Gálvez-Barrón et al. Sci Rep 2023 13(1) 12709 - Using random forest machine learning on data from a large, representative cohort of the general population improves clinical spirometry references.
Kris Kristensen et al. Clin Respir J 2023 - Experimental drugs in clinical trials for COPD: Artificial Intelligence via Machine Learning approach to predict the successful advance from early-stage development to approval.
Luigino Calzetta et al. Expert Opin Investig Drugs 2023 - A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence.
Engin Melekoglu et al. PeerJ Comput Sci 2023 8e1188 - Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.
Johan N Siebert et al. BMC Pulm Med 2023 23(1) 191 - Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV decline: results from COPDGene and SPIROMICS.
Jennifer M Wang et al. Front Physiol 2023 141144192 - Lung and Gut Microbiome in COPD
E Karakasidis et al, J Per Med, May 8, 2023 - Deep learning model improves COPD risk prediction and gene discovery.
et al. Nat Genet 2023 4 - Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models.
Justin Cosentino et al. Nat Genet 2023 4 - Modelling 30-day hospital readmission after discharge for COPD patients based on electronic health records.
Meng Li et al. NPJ Prim Care Respir Med 2023 33(1) 16 - Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials.
Kenneth Verstraete et al. Thorax 2023 - Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk.
Nick Shrine et al. Nature genetics 2023 3 (3) 410-422 - Development and validation of a respiratory-responsive vocal biomarker-based tool for generalizable detection of respiratory impairment: independent case-control studies in multiple respiratory conditions including asthma, chronic obstructive pulmonary disease, and COVID-19.
Savneet Kaur et al. Journal of medical Internet research 2023 - Artificial intelligence based software facilitates spirometry quality control in asthma and COPD clinical trials.
Eva Topole et al. ERJ open research 2023 9(1)
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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:Apr 25, 2024
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