Non-Genomics Precision Health Update

Genomics & Precision Health Database| the Genomics & Health Impact Weekly Scan|PHGKB

Last Update Date: Nov 24, 2019

Non-Genomics Precision Health Update

The latest information and publications on the impact of big data science, machine learning and predictive analytics on public health.

• Machine Learning, Predictive Analytics, and Clinical Practice Can the Past Inform the Present?
  EC Peterson, JAMA, November 22, 2019
• Addressing Bias in Artificial Intelligence in Health Care
  R Parikh et al, JAMA, NOvember 21, 2019
• Lifecycle Regulation of Artificial Intelligence– and Machine Learning–Based Software Devices in Medicine
  TJ Hwang et al, JAMA, November 22, 2019
• Artificial intelligence for global health
  A Hosny et al, Science, November 21, 2019
• AI-augmented multidisciplinary teams: hype or hope?
  A di Leva, The Lancet Digital Health, November 2019
• Quantifying normal and parkinsonian gait features from home movies: Practical application of a deep learning-based 2D pose estimator.
  Sato Kenichiro et al. PloS one 2019 14(11) e0223549
• 3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.
  Wei Ye et al. PloS one 2019 14(11) e0225041
• Translational Health Disparities Research in a Data-Rich World.
  Breen Nancy et al. Health equity 2019 3(1) 588-600
• Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm.
  Li Feng et al. Translational vision science & technology 2019 Nov 8(6) 4
• Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks.
  Hamwood Jared et al. Translational vision science & technology 2019 Nov 8(6) 10
• [Actively promoting the research and development of artificial intelligence diagnosis and treatment of orbital disease].
  Fan X Q et al. [Zhonghua yan ke za zhi] Chinese journal of ophthalmology 2019 Nov 55(11) 801-805
• Towards a wearable system for predicting the freezing of gait in people affected by Parkinson's disease.
  Demrozi Florenc et al. IEEE journal of biomedical and health informatics 2019 Nov
• A machine learning algorithm successfully screens for Parkinson's in web users.
  Youngmann Brit et al. Annals of clinical and translational neurology 2019 Nov
• Application of machine learning methodology to assess the performance of DIABETIMSS program for patients with type 2 diabetes in family medicine clinics in Mexico.
  You Yue et al. BMC medical informatics and decision making 2019 Nov 19(1) 221
• Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-Transplant Patients.
  Improta Giovanni et al. Journal of evaluation in clinical practice 2019 Nov
• Improving risk prediction in heart failure using machine learning.
  Adler Eric D et al. European journal of heart failure 2019 Nov
• Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography.
  Zreik Majd et al. IEEE transactions on medical imaging 2019 Nov
• A Systematic Review of Detecting Sleep Apnea Using Deep Learning.
  Mostafa Sheikh Shanawaz et al. Sensors (Basel, Switzerland) 2019 Nov 19(22)
• Human versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.
  Jammal Alessandro A et al. American journal of ophthalmology 2019 Nov
• An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study.
  Williams Bryan M et al. Diabetologia 2019 Nov

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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.