Last Posted: Sep 02, 2022
- Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population.
Liu Qing et al. BMC pulmonary medicine 2022 22(1) 327
- Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model.
Karampitsakos Theodoros et al. Respiration; international review of thoracic diseases 2021 1-10
- Carbon Monoxide
FromATSDR Toxic Substances Portal
- Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
Giri Paresh C et al. Frontiers in physiology 2021 12678540
- Predicting Lung Cancer in the United States: A Multiple Model Examination of Public Health Factors.
Kamis Arnold et al. International journal of environmental research and public health 2021 18(11)
- Effects of air pollution in Spatio-temporal modeling of asthma-prone areas using a machine learning model.
Razavi-Termeh Seyed Vahid et al. Environmental research 2021 111344
- Psychological impact of genetic and clinical screening for pulmonary fibrosis on asymptomatic first-degree relatives of affected individuals.
Carmichael Nikkola et al. Thorax 2021 Jan
- Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.
Chassagnon Guillaume et al. Radiology 2020 Oct 200319
- Ensemble-based deep learning for estimating PM 2.5 over California with multisource big data including wildfire smoke.
Li Lianfa et al. Environment international 2020 Sep 145106143
- A randomized controlled trial of a smoking cessation smartphone application with a carbon monoxide checker
K Masaki et al, NPJ Digital Medicine, March 12, 2020
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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.