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

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

Last Update Date: Jul 20, 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.

• Prediction and Prevention Using Deep Learning.
  Tsega Surafel et al. JAMA network open 2019 Jul 2(7) e197447
• Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization.
  Khosravi Pegah et al. NPJ digital medicine 2019 221
• Artificial Intelligence for Clinical Trial Design
  S Harrer et al, Trends in Pharma Sci, July 17, 2019
• Bayesian Approach to Incorporating Different Types of Biomedical Knowledge Bases into Information Retrieval Systems for Clinical Decision Support in Precision Medicine.
  Balaneshinkordan Saeid et al. Journal of biomedical informatics 2019 Jul 103238
• Using data science to diagnose and characterize heterogeneity of Alzheimer's disease.
  Ang Ting F A et al. Alzheimer's & dementia (New York, N. Y.) 2019 5264-271
• Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data
  Johnston IG, et. al. npj Digital Medicine 2, Article number: 63 (2019)
• Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media.
  Du Jingcheng et al. NPJ digital medicine 2019 227
• Doctors Are Turning to Artificial Intelligence to Diagnose Cancer Patients
  DH Freedman, Newsweek, July 16, 2019
• Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health?
  Khoury MJ et al, Public Health Genomics, July 17, 2019
• Skin sensors are the future of health care
  S Xu et al, Nature News, July 17, 2019
• Use of big data in drug development for precision medicine: an update.
  Qian Tongqi et al. Expert review of precision medicine and drug development 2019 4(3) 189-200
• MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
  Yi Xiaoping et al. Frontiers in oncology 2019 9552
• Benchmark on a large cohort for sleep-wake classification with machine learning techniques.
  Palotti Joao et al. NPJ digital medicine 2019 250
• Patients' views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort.
  Tran Viet-Thi et al. NPJ digital medicine 2019 253
• Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning.
  Kuo Chin-Chi et al. NPJ digital medicine 2019 229
• Improving clinical refractive results of cataract surgery by machine learning.
  Sramka Martin et al. PeerJ 2019 7e7202
• Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program.
  Raumviboonsuk Paisan et al. NPJ digital medicine 2019 225
• Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance.
  Tian Xiaolu et al. Computational and mathematical methods in medicine 2019 20196915850
• Artificial intelligence algorithm for predicting mortality of patients with acute heart failure.
  Kwon Joon-Myoung et al. PloS one 2019 14(7) e0219302
• Three Problems with Big Data and Artificial Intelligence in Medicine.
  Chin-Yee Benjamin et al. Perspectives in biology and medicine 2019 62(2) 237-256

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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.