Last Update Date: Aug 03, 2019
- Artificial Intelligence and Training Physicians to Perform Technical Procedures
G Shorten JAMA Network Open, AUgust 2, 2019 - Identifying Smoking Environments From Images of Daily Life With Deep Learning
MM Engelhard et al, JAMA Network Open, August 2, 2019 - Toward an accelerated adoption of data-driven findings in medicine : Research, skepticism, and the need to speed up public visibility of data-driven findings.
Kartoun Uri et al. Medicine, health care, and philosophy 2019 Mar 22(1) 153-157 - The analysis of the effects of acute rheumatic fever in childhood on cardiac disease with data mining.
Emre Ilkim Ecem et al. International journal of medical informatics 2019 12368-75 - Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models.
Muñoz Emir et al. Briefings in bioinformatics 2019 20(1) 190-202 - The value of a health insurance database to conduct pharmacoepidemiological studies in oncology.
Conte Cécile et al. Therapie 2019 Apr 74(2) 279-288 - Integration of genetic and clinical information to improve imputation of data missing from electronic health records.
Li Ruowang et al. Journal of the American Medical Informatics Association : JAMIA 2019 Apr - Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.
Cruz-Roa Angel et al. Scientific reports 2017 746450 - Eigen Posture Based Fall Risk Assessment System Using Kinect.
Tripathy Soumya Ranjan et al. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference 2018 20181-4 - Postoperative seizure outcome-guided machine learning for interictal electrocorticography in neocortical epilepsy.
Park Seong-Cheol et al. Journal of neurophysiology 2018 119(6) 2265-2275 - Machine Learning Approaches for Clinical Psychology and Psychiatry.
Dwyer Dominic B et al. Annual review of clinical psychology 2018 1491-118 - Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity.
Patel J et al. Methods of information in medicine 2018 57(5-06) 253-260 - Application of Machine Learning to Predict Dietary Lapses During Weight Loss.
Goldstein Stephanie P et al. Journal of diabetes science and technology 2018 12(5) 1045-1052 - The Potential of West Nile Virus Transmission Regarding the Environmental Factors Using Geographic Information System (GIS), West Azerbaijan Province, Iran.
Amini Mojtaba et al. Journal of arthropod-borne diseases 2019 Mar 13(1) 27-38 - Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort.
Leightley Daniel et al. Journal of mental health (Abingdon, England) 2019 Feb 28(1) 34-41 - Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model.
Hou Ruichao et al. Medical & biological engineering & computing 2019 Apr 57(4) 887-900 - Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms.
Mesallam Tamer A et al. Journal of healthcare engineering 2017 20178783751 - Therapy Decision Support Based on Recommender System Methods.
Gräßer Felix et al. Journal of healthcare engineering 2017 20178659460 - Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.
Hong Huixiao et al. Scientific reports 2017 7(1) 17311 - Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.
Wang Xinggang et al. Scientific reports 2017 7(1) 15415
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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