Last Update Date: Dec 16, 2019
- Why digital health has been such a disappointment, and how to change that
N Khoshla, CNBC, December 15, 2019 - Machine Learning and the Cancer-Diagnosis Problem - No Gold Standard.
Adamson Adewole S et al. The New England journal of medicine 2019 Dec 381(24) 2285-2287 - Digital clinical trials: creating a vision for the future
SR Steinhubl et al, NPJ Digital Medicine, December 12, 2019 - Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors
BR Greene, NPJ Digital Medicine, December 2019 - Making Machine Learning Accessible and Actionable for Clinicians.
Schneider David F et al. JAMA network open 2019 Dec 2(12) e1917362 - Artificial Intelligence and Surgical Decision-Making.
Loftus Tyler J et al. JAMA surgery 2019 Dec - Mass-producing wearable sensors: No sweat
LH Han, Sci Trans Med, December 11, 2019 - Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.
Dauvin Antonin et al. NPJ digital medicine 2019 2116 - Artificial intelligence for precision medicine in neurodevelopmental disorders.
Uddin Mohammed et al. NPJ digital medicine 2019 2112 - Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.
Alexander Joe et al. Pragmatic and observational research 2019 1067-76 - Establishment of a new non-invasive imaging prediction model for liver metastasis in colon cancer.
Li Yu et al. American journal of cancer research 2019 9(11) 2482-2492 - Big Data in Medicine, the Present and Hopefully the Future.
Riba Michela et al. Frontiers in medicine 2019 6263 - Attitudes Of Chinese Cancer Patients Toward The Clinical Use Of Artificial Intelligence.
Yang Keyi et al. Patient preference and adherence 2019 131867-1875 - Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes.
Liu Yujia et al. Risk management and healthcare policy 2019 12189-198 - A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms.
De Man Quinten et al. Medical physics 2019 Dec 46(12) e790-e800 - Corneal thickness measurement by secondary speckle tracking and image processing using machine-learning algorithms.
Bennett Aviya et al. Journal of biomedical optics 2019 24(12) 1-10 - Artificial intelligence and algorithmic bias: implications for health systems.
Panch Trishan et al. Journal of global health 2019 Dec 9(2) 010318 - Evaluation of colorectal cancer subtypes and cell lines using deep learning.
Ronen Jonathan et al. Life science alliance 2019 Dec 2(6) - Deep learning techniques for detecting preneoplastic and neoplastic lesions in human colorectal histological images.
Sena Paola et al. Oncology letters 2019 Dec 18(6) 6101-6107 - Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples.
Khan Atif et al. Journal of occupational and environmental medicine 2019 Dec 61 Suppl 12S55-S64
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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