Last Update Date: Nov 30, 2019
- Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients
A Deauvin et al, NPJ Digital Medicine, November 29, 2019 - Epidemiologic Research in Dry Eye Disease and the Utility of Mobile Health Technology
MTM Wang et al, JAMA Ophthalmology, November 28 2019 - Epidemiologic Research in Dry Eye Disease and the Utility of Mobile Health Technology
MTM Wang et al, JAMA Ophthalmology, November 28 2019 - Using urinary biomarkers to reduce acute kidney injury following cardiac surgery.
Engelman Daniel T et al. The Journal of thoracic and cardiovascular surgery 2019 Oct - Prospective assessment of contralateral prophylactic mastectomy decision-making in women with average risk: an application of perceptual mapping.
Greener Judith R et al. Translational behavioral medicine 2019 Nov - Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density.
Mullooly Maeve et al. NPJ breast cancer 2019 543 - Human-machine partnership with artificial intelligence for chest radiograph diagnosis.
Patel Bhavik N et al. NPJ digital medicine 2019 2111 - Advancing the Promise of Digital Technology and Social Media to Promote Population Health.
Allegrante John P et al. Health education & behavior : the official publication of the Society for Public Health Education 2019 Dec 46(2_suppl) 5-8 - Achieving Rapid Blood Pressure Control With Digital Therapeutics: Retrospective Cohort and Machine Learning Study.
Guthrie Nicole L et al. JMIR cardio 2019 Mar 3(1) e13030 - Quantification of hepatic steatosis in histologic images by deep learning method.
Yang Fan et al. Journal of X-ray science and technology 2019 Nov - Self-attention based recurrent convolutional neural network for disease prediction using healthcare data.
Usama Mohd et al. Computer methods and programs in biomedicine 2019 Nov 105191 - Identifying predictors of probable posttraumatic stress disorder in children and adolescents with earthquake exposure: A longitudinal study using a machine learning approach.
Ge Fenfen et al. Journal of affective disorders 2019 Nov - Reaching families where a parent has a mental disorder: Using big data to plan early interventions.
Zechmeister-Koss Ingrid et al. Neuropsychiatrie : Klinik, Diagnostik, Therapie und Rehabilitation : Organ der Gesellschaft Osterreichischer Nervenarzte und Psychiater 2019 Nov - Implementing machine learning in bipolar diagnosis in China.
Ma Yantao et al. Translational psychiatry 2019 Nov 9(1) 305 - Improving the geographical precision of rural chronic disease surveillance by using emergency claims data: a cross-sectional comparison of survey versus claims data in Sullivan County, New York.
Lee David C et al. BMJ open 2019 Nov 9(11) e033373 - A Bayesian machine learning approach for drug target identification using diverse data types.
Madhukar Neel S et al. Nature communications 2019 Nov 10(1) 5221 - Multimodal feature learning and fusion on B-mode ultrasonography and sonoelastography using point-wise gated deep networks for prostate cancer diagnosis.
Zhang Qi et al. Biomedizinische Technik. Biomedical engineering 2019 Nov - A systematic review of mHealth funded R&D activities in EU: Trends, technologies and obstacles.
Koumpouros Yiannis et al. Informatics for health & social care 2019 Nov 1-20 - On the ethics of algorithmic decision-making in healthcare.
Grote Thomas et al. Journal of medical ethics 2019 Nov - Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks.
Jiang Han-Jing et al. Journal of translational medicine 2019 Nov 17(1) 382
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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