Last Update Date: Dec 21, 2019
- Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.
Commandeur Frederic et al. Cardiovascular research 2019 Dec - Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.
Commandeur Frederic et al. Cardiovascular research 2019 Dec - Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation
A Wen et al, NPJ Digital Medicine, December 2019 - Artificial Intelligence in Health Care- A Report From the National Academy of Medicine
ME Matheny et al, JAMA, December 17, 2019 - Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril
A Special Publication from the National Academy of Medicine - Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning.
van Amsterdam W A C et al. NPJ digital medicine 2019 2122 - Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test.
Abbas Hasan T et al. PloS one 2019 14(12) e0219636 - Prophylactic antibiotic bundle compliance and surgical site infections: an artificial neural network analysis.
Walczak Steven et al. Patient safety in surgery 2019 1341 - Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis.
Chitnis Tanuja et al. NPJ digital medicine 2019 2123 - Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method.
Feng Qi et al. Frontiers in aging neuroscience 2019 11323 - Data mining polycystic ovary morphology in electronic medical record ultrasound reports.
Cheng Jay Jojo et al. Fertility research and practice 2019 513 - Making Sense of Pharmacovigilance and Drug Adverse Event Reporting: Comparative Similarity Association Analysis Using AI Machine Learning Algorithms in Dogs and Cats.
Xu Xuan et al. Topics in companion animal medicine 2019 Dec 37100366 - Ethical considerations about artificial intelligence for prognostication in intensive care.
Beil Michael et al. Intensive care medicine experimental 2019 Dec 7(1) 70 - Feasibility study for use of angiographic parametric imaging and deep neural networks for intracranial aneurysm occlusion prediction.
Shiraz Bhurwani Mohammad Mahdi et al. Journal of neurointerventional surgery 2019 Dec - Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.
Huang Shigao et al. Cancer letters 2019 Dec 47161-71 - DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks-application to CEST imaging of the human brain at 3T.
Glang Felix et al. Magnetic resonance in medicine 2019 Dec - Using machine learning models to improve stroke risk level classification methods of China national stroke screening.
Li Xuemeng et al. BMC medical informatics and decision making 2019 Dec 19(1) 261 - Evaluating the reliability of neurocognitive biomarkers of neurodegenerative diseases across countries: A machine learning approach.
Bachli M Belen et al. NeuroImage 2019 Dec 116456 - EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes.
Zheng Li et al. IEEE transactions on medical imaging 2019 Dec - Artificial intelligence applications for thoracic imaging.
Chassagnon Guillaume et al. European journal of radiology 2019 Dec 123108774
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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