Last Update Date: Feb 16, 2020
- Disease modelers gaze into their computers to see the future of Covid-19, and it isn’t good
S Begeley, StatNews, February 14, 2020 - Estimating underdetection of internationally imported COVID-19 cases
PM De Salazaar et al, MEDRXIV, February 2020 - COVID-19 Resource Center
The Lancet, February 2020 - Preliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019-nCoV.
Zhou Tao et al. Journal of evidence-based medicine 2020 Feb - CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction
P Vaidya et al, Lancet Digital Health, February 13, 2020 - Opportunities and challenges to advance the use of electronic patient-reported outcomes in clinical care: a report from AMIA workshop proceedings.
Austin Elizabeth et al. JAMIA open 2019 Dec 2(4) 407-410 - Toward a precision behavioral medicine approach to addressing high-risk sun exposure: a qualitative analysis.
Stump Tammy K et al. JAMIA open 2019 Dec 2(4) 547-553 - Using natural language processing to construct a metastatic breast cancer cohort from linked cancer registry and electronic medical records data.
Ling Albee Y et al. JAMIA open 2019 Dec 2(4) 528-537 - Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.
Loo Jessica et al. Ophthalmology 2019 Dec - Detecting and interpreting myocardial infarction using fully convolutional neural networks.
Strodthoff Nils et al. Physiological measurement 2019 40(1) 015001 - Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.
Deng Huiyu et al. BMC medical research methodology 2019 19(1) 70 - Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks.
Eppenhof Koen A J et al. IEEE transactions on medical imaging 2019 38(5) 1097-1105 - Preparing next-generation scientists for biomedical big data: artificial intelligence approaches.
Moore Jason H et al. Personalized medicine 2019 16(3) 247-257 - A Novel Air Quality Early-Warning System Based on Artificial Intelligence.
Mo Xinyue et al. International journal of environmental research and public health 2019 16(19) - A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments During Various Locomotion Tasks.
Stetter Bernd J et al. Frontiers in bioengineering and biotechnology 2020 89 - Medical Image Synthesis via Deep Learning.
Yu Biting et al. Advances in experimental medicine and biology 2020 121323-44 - Machine Learning to Predict the 1-Year Mortality Rate After Acute Anterior Myocardial Infarction in Chinese Patients.
Li Yi-Ming et al. Therapeutics and clinical risk management 2020 161-6 - Out of the Clinic, into the Home: The in-Home Use of Phantom Motor Execution Aided by Machine Learning and Augmented Reality for the Treatment of Phantom Limb Pain.
Lendaro Eva et al. Journal of pain research 2020 13195-209 - The National Institutes of Health funding for clinical research applying machine learning techniques in 2017.
Annapureddy Amarnath R et al. NPJ digital medicine 2020 313 - Precise hepatectomy in the intelligent digital era.
Chen Hao et al. International journal of biological sciences 2020 16(3) 365-373
Disclaimer: Articles listed in Non-Genomics Precision Health Update are selected by the CDC Office of Public Health Genomics to provide current awareness of the scientific 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 Clips, 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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