Last Update Date: Jul 26, 2019
- General practice by smartphone.
Roland Martin et al. BMJ (Clinical research ed.) 2019 366l4713 - Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty.
Karhade Aditya V et al. The Journal of arthroplasty 2019 Jun - Estimating the success of re-identifications in incomplete datasets using generative models
Luc Rocher, et al. Nature Communications 10, Article number: 3069 (2019) - City-wide electronic health records reveal gender and age biases in administration of known drug–drug interactions
Correia RB, et al. Digital Medicine Volume 2, Article number: 74 (2019) - Estimating the success of re-identifications in incomplete datasets using generative models
Rocher L, et al. Nature Communications Volume 10, Article number: 3069 (2019) - City-wide electronic health records reveal gender and age biases in administration of known drug–drug interactions
Correia RB, et al. Digital Medicine Volume 2, Article number: 74 (2019) - Membranous Nephropathy and Anti-Podocytes Antibodies: Implications for the Diagnostic Workup and Disease Management.
Pozdzik Agnieszka et al. BioMed research international 2018 20186281054 - Harnessing digital technology to predict, diagnose, monitor, and develop treatments for brain disorders.
Stroud Clare et al. NPJ digital medicine 2019 244 - Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective.
Zhang Sushen et al. IEEE reviews in biomedical engineering 2019 12194-208 - Twitter Conversations and English News Media Reports on Poliomyelitis in Five Different Countries, January 2014 to April 2015.
Schaible Braydon J et al. The Permanente journal 2019 23 - Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients.
Afshar Majid et al. PloS one 2019 14(7) e0219717 - Deep Learning in Cardiology.
Bizopoulos Paschalis et al. IEEE reviews in biomedical engineering 2019 12168-193 - Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance.
Edo-Osagie Oduwa et al. PloS one 2019 14(7) e0210689 - Reviewing ensemble classification methods in breast cancer.
Hosni Mohamed et al. Computer methods and programs in biomedicine 2019 Aug 17789-112 - An empirical evaluation of deep learning for ICD-9 code assignment using MIMIC-III clinical notes.
Huang Jinmiao et al. Computer methods and programs in biomedicine 2019 Aug 177141-153 - Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
Pérez-Benito Francisco Javier et al. Computer methods and programs in biomedicine 2019 Aug 177123-132 - Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing.
Garg Ravi et al. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 2019 Jul 28(7) 2045-2051 - Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals.
Rosas-Romero Roberto et al. Computers in biology and medicine 2019 Jul 111103355 - Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.
Ruijsink Bram et al. JACC. Cardiovascular imaging 2019 Jul - A spatio-temporally weighted hybrid model to improve estimates of personal PM 2.5 exposure: Incorporating big data from multiple data sources.
Ben YuJie et al. Environmental pollution (Barking, Essex : 1987) 2019 Jul 253403-411
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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