Last Update Date: Nov 17, 2019
- Reporting on deep learning algorithms in health care
M Yu et al, Lancet Digital Health, November 2019 - Can skin cancer diagnosis be transformed by AI?
A Esteva et al, Lancet Digital Health, November 2019 - 3 Myths About Machine Learning in Health Care
DA Haas et al, Harvard Business Review, November 2019 - Utility of a public-available artificial intelligence in diagnosis of polypoidal choroidal vasculopathy.
Yang Jingyuan et al. Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie 2019 Nov - Machine Learning-Based Prediction Models for 30-Day Readmission after Hospitalization for Chronic Obstructive Pulmonary Disease.
Goto Tadahiro et al. COPD 2019 Nov 1-6 - ASO Author Reflections: Resection for Hepatocellular Carcinoma Beyond the BCLC Guidelines-How Can Machine Learning Techniques Help?
Tsilimigras Diamantis I et al. Annals of surgical oncology 2019 Nov - Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline.
Bonavita Ilaria et al. Computer methods and programs in biomedicine 2019 Nov 185105172 - Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.
Wang Y et al. JDR clinical and translational research 2019 Nov 2380084419885612 - Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.
Scannell Cian M et al. Journal of magnetic resonance imaging : JMRI 2019 Nov - The Use of Optical Coherence Tomography and Convolutional Neural Networks to Distinguish Normal and Abnormal Oral Mucosa.
Heidari Andrew E et al. Journal of biophotonics 2019 Nov - A machine-learning approach to predict postprandial hypoglycemia.
Seo Wonju et al. BMC medical informatics and decision making 2019 Nov 19(1) 210 - Deep Learning on Multi Sensor Data for Counter UAV Applications-A Systematic Review.
Samaras Stamatios et al. Sensors (Basel, Switzerland) 2019 Nov 19(22) - Reaching Those at Highest Risk for Suicide: Development of a Model Using Machine Learning Methods for use With Native American Communities.
Haroz Emily E et al. Suicide & life-threatening behavior 2019 Nov - Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach.
Sun Ruopeng et al. Scientific reports 2019 Nov 9(1) 16154 - Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images.
Miyagi Yasunari et al. Molecular and clinical oncology 2019 Dec 11(6) 583-589 - Machine Learning-Based Framework for Differential Diagnosis Between Vascular Dementia and Alzheimer's Disease Using Structural MRI Features.
Zheng Yineng et al. Frontiers in neurology 2019 101097 - Utilizing Machine Learning for Pre- and Postoperative Assessment of Patients Undergoing Resection for BCLC-0, A and B Hepatocellular Carcinoma: Implications for Resection Beyond the BCLC Guidelines.
Tsilimigras Diamantis I et al. Annals of surgical oncology 2019 Nov - The effect of climate change on cholera disease: The road ahead using artificial neural network.
Asadgol Zahra et al. PloS one 2019 14(11) e0224813 - Language impairment in adults with end-stage liver disease: application of natural language processing towards patient-generated health records.
Dickerson Lindsay K et al. NPJ digital medicine 2019 2106 - Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications.
Barnett Scott et al. Journal of medical Internet research 2019 Nov 21(11) e16399
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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