Last Update Date: Oct 05, 2019
- Potential Liability for Physicians Using Artificial Intelligence
WN Price et al, JAMA, October 4, 2019 - What to expect from AI in oncology.
et al. Nature reviews. Clinical oncology 2019 Oct - Current applications of big data and machine learning in cardiology.
Cuocolo Renato et al. Journal of geriatric cardiology : JGC 2019 Aug 16(8) 601-607 - Precision public health and HIV in Africa.
Blower Sally et al. The Lancet. Infectious diseases 2019 Oct 19(10) 1050-1052 - Technology Tools: Increasing Our Reach in National Surveillance of Intellectual and Developmental Disabilities.
Wagner Jordan B et al. Intellectual and developmental disabilities 2019 Oct 57(5) 463-475 - Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.
Ramaswamy Sowmya M et al. British journal of anaesthesia 2019 123(4) 479-487 - A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis.
et al. Circulation. Cardiovascular imaging 2019 Oct 12(10) e009214 - Predicting gestational personal exposure to PM 2.5 from satellite-driven ambient concentrations in Shanghai.
Zhu Qingyang et al. Chemosphere 2019 Oct 233452-461 - Computer Algorithms in Assessment of Obstructive Sleep Apnoea Syndrome and Its Application in Estimating Prevalence of Sleep Related Disorders in Population.
Katyayan Angira et al. Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India 2019 Sep 71(3) 352-359 - Make Intelligent of Gastric Cancer Diagnosis Error in Qazvin's Medical Centers: Using Data Mining Method.
Mortezagholi Asghar et al. Asian Pacific journal of cancer prevention : APJCP 2019 20(9) 2607-2610 - Deep Learning in Automated Region Proposal and Diagnosis of Chronic Otitis Media Based on Computed Tomography.
Wang Yan-Mei et al. Ear and hearing 2019 Sep - Quadruple Decision Making for Parkinson's Disease Patients: Combining Expert Opinion, Patient Preferences, Scientific Evidence, and Big Data Approaches to Reach Precision Medicine.
van den Heuvel Lieneke et al. Journal of Parkinson's disease 2019 Sep - Artificial Intelligence Distinguishes Surgical Training Levels in a Virtual Reality Spinal Task.
Bissonnette Vincent et al. The Journal of bone and joint surgery. American volume 2019 Sep - Personalized prediction of live birth prior to the first in vitro fertilization treatment: a machine learning method.
Qiu Jiahui et al. Journal of translational medicine 2019 Sep 17(1) 317 - Individualized prediction of depressive disorder in the elderly: A multitask deep learning approach.
Xu Zhongzhi et al. International journal of medical informatics 2019 Sep 132103973 - Patient Clustering Improves Efficiency of Federated Machine Learning to Predict Mortality and Hospital Stay Time Using Distributed Electronic Medical Records.
Huang Li et al. Journal of biomedical informatics 2019 Sep 103291 - Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.
Phene Sonia et al. Ophthalmology 2019 Sep - Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.
Geras Krzysztof J et al. Radiology 2019 Sep 182627 - Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium after Cardiac Surgery.
Mufti Hani Nabeel et al. JMIR medical informatics 2019 Sep - Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography.
Sheth Sunil A et al. Stroke 2019 Sep STROKEAHA119026189
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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