Last Update Date: Oct 11, 2019
- Machine Learning Goes Mainstream: PLOS Medicine 15th Anniversary
PLOS Blogs, October 8, 2019 - Wearable technology and lifestyle management: the fight against obesity and diabetes
The Lancet Digital Health, Vol 1, Iss 6, Pe 243, October 1, 2019 - A Theoretical Framework for Clinical Implementation of Social Determinants of Health.
Hammond Gmerice et al. JAMA cardiology 2019 Oct - Harnessing data and technology for public health: five challenges
- Medical device surveillance with electronic health records.
Callahan Alison et al. NPJ digital medicine 2019 294 - Use of Non-invasive Parameters and Machine-Learning Algorithms for Predicting Future Risk of Type 2 Diabetes: A Retrospective Cohort Study of Health Data From Kuwait.
Farran Bassam et al. Frontiers in endocrinology 2019 10624 - Differentiating Noninvasive Follicular Thyroid Neoplasm with Papillary-Like Nuclear Features from Classic Papillary Thyroid Carcinoma: Analysis of Cytomorphologic Descriptions Using a Novel Machine-Learning Approach.
Maleki Sara et al. Journal of pathology informatics 2019 1029 - Towards scaling Twitter for digital epidemiology of birth defects.
Klein Ari Z et al. NPJ digital medicine 2019 296 - KETOS: Clinical decision support and machine learning as a service - A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services.
Gruendner Julian et al. PloS one 2019 14(10) e0223010 - Can machine learning improve patient selection for cardiac resynchronization therapy?
Hu Szu-Yeu et al. PloS one 2019 14(10) e0222397 - Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.
Geis J Raymond et al. Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes 2019 Oct - Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement.
Geis J Raymond et al. Insights into imaging 2019 Oct 10(1) 101 - Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement.
Geis J Raymond et al. Radiology 2019 191586 - Insights into Amyotrophic Lateral Sclerosis from a Machine Learning Perspective.
Gordon Jonathan et al. Journal of clinical medicine 2019 Oct 8(10) - Use of Patient-Reported Data to Match Depression Screening Intervals With Depression Risk Profiles in Primary Care Patients With Diabetes: Development and Validation of Prediction Models for Major Depression.
Jin Haomiao et al. JMIR formative research 2019 Oct 3(4) e13610 - Machine Learning in Epidemiology and Health Outcomes Research.
Wiemken Timothy L et al. Annual review of public health 2019 Oct - Predicting emergency department orders with multilabel machine learning techniques and simulating effects on length of stay.
Hunter-Zinck Haley S et al. Journal of the American Medical Informatics Association : JAMIA 2019 Oct - Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework.
Sarker Abeed et al. Journal of the American Medical Informatics Association : JAMIA 2019 Oct - 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.
Henry Sam et al. Journal of the American Medical Informatics Association : JAMIA 2019 Oct - Convolutional Neural Network for Differentiating Gastric Cancer from Gastritis Using Magnified Endoscopy with Narrow Band Imaging.
Horiuchi Yusuke et al. Digestive diseases and sciences 2019 Oct
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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