Last Update Date: Jan 20, 2020
- Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation
P Tiwari et al, JAMA Network Open, January 17, 2020 - The dark side of digital health
I Kickbusch, BMJ, January 2020 - mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson’s disease
MK Erb et al, NPJ Digital Medicine, January 2020 - Study to use artificial intelligence to explore suicide risk
P Govern, VUMC Reporter, January 15, 2020 - Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study
JM Radin et al, Lancet Digital HEalth, January 16, 2020 - Machine Learning in Fetal Cardiology: What to Expect.
Garcia-Canadilla Patricia et al. Fetal diagnosis and therapy 2020 Jan 1-10 - Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation.
Fellous Jean-Marc et al. Frontiers in neuroscience 2019 131346 - Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer's disease status.
Joshi Prajakta S et al. Alzheimer's & dementia (New York, N. Y.) 2019 5964-973 - Convolution neural network-based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation.
Basheera Shaik et al. Alzheimer's & dementia (New York, N. Y.) 2019 5974-986 - Feasibility of Multimodal MRI-Based Deep Learning Prediction of High Amino Acid Uptake Regions and Survival in Patients With Glioblastoma.
Jeong Jeong-Won et al. Frontiers in neurology 2019 101305 - Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques.
Lai Nai-Hua et al. Computer methods and programs in biomedicine 2019 Dec 188105307 - Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.
Alabi Rasheed Omobolaji et al. International journal of medical informatics 2019 Dec 136104068 - Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.
McKinley Richard et al. NeuroImage. Clinical 2019 Dec 25102104 - Development and validation of deep learning algorithms for scoliosis screening using back images.
Yang Junlin et al. Communications biology 2019 Oct 2(1) 390 - Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging.
Teo Jing Xian et al. Communications biology 2019 Oct 2(1) 361 - Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks.
Mousavi Sajad et al. PloS one 2020 15(1) e0226990 - Detection of Malignant Melanoma Using Artificial Intelligence: An Observational Study of Diagnostic Accuracy.
Phillips Michael et al. Dermatology practical & conceptual 2020 10(1) e2020011 - Diastolic Function Evaluation: What Can We Learn From Machine Learning?
Kampaktsis Polydoros N et al. JACC. Cardiovascular imaging 2020 Jan 13(1 Pt 2) 336-337 - An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging.
Alexander Alan et al. Journal of the American College of Radiology : JACR 2020 Jan 17(1 Pt B) 165-170 - Deep learning for electronic health records: A comparative review of multiple deep neural architectures.
Ayala Solares Jose Roberto et al. Journal of biomedical informatics 2020 Jan 101103337
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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