Genomics & Precision Health Database| the Genomics & Health Impact Weekly Scan|PHGKB
Last Update Date: Sep 30, 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.
- Data sharing is key to innovation in health care
MIT Tech Review, September 27, 2019
- An awakening in medicine: the partnership of humanity and intelligent machines
LA Celi et al, Lancet Digital Health, September 27, 2019
- Digital health: From clinical trials to diagnosis and surgery, artificial intelligence has the potential to transform medicine.
R Hodson, Nature Outlook, September 25, 2019
- A fairer way forward for AI in health care
L Nordling, Nature Outlook, September 25, 2019
- The future of electronic health records
J Hecht, Nature Outlook, September 25, 2019
- Deep learning algorithm predicts diabetic retinopathy progression in individual patients
F Arcadu et al, NPJ Digital Medicine, September 20, 2019
- Medical device surveillance with electronic health records
A Callahan et al, NPJ Digital Medicine, September 25, 2019
- Feasibility and utility of a clinician dashboard from wearable and mobile application Parkinson’s disease data
JJ Elm et al, NPJ Digital Medicine, September 25, 2019
- Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed
The CONSORT-AI and SPIRIT-AI Steering Group, Nature Medicine, September 25, 2019
- Human versus machine in medicine: can scientific literature answer the question?
TS Cook, Lancet Digital Health, September 24, 2019
- A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.
Bechel Meagan A et al. PloS one 2019 14(9) e0222826
- Symptom-specific effectiveness of an internet-based intervention in the treatment of mild to moderate depressive symptomatology: The potential of network estimation techniques.
Boschloo Lynn et al. Behaviour research and therapy 2019 Aug 122103440
- Machine Learning Models Identify Multimodal Measurements Highly Predictive of Transdiagnostic Symptom Severity for Mood, Anhedonia, and Anxiety.
Mellem Monika S et al. Biological psychiatry. Cognitive neuroscience and neuroimaging 2019 Jul
- Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes.
Bao Yujia et al. JCO clinical cancer informatics 2019 Sep 31-9
- [Artificial intelligence: a benefit for patients?]
Marsico Giovanna et al. Soins; la revue de reference infirmiere 2019 Sep 64(838) 40-41
- Big Data for Nutrition Research in Pediatric Oncology: Current State and Framework for Advancement.
Phillips Charles A et al. Journal of the National Cancer Institute. Monographs 2019 Sep 2019(54) 127-131
- Machine learning in psychiatry- standards and guidelines.
Tandon Neeraj et al. Asian journal of psychiatry 2019 Sep
- Using text mining to extract depressive symptoms and to validate the diagnosis of major depressive disorder from electronic health records.
Wu Chi-Shin et al. Journal of affective disorders 2019 Sep 260617-623
- Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration.
Ghavami Nooshin et al. Medical image analysis 2019 Sep 58101558
- Machine learning for radiomics-based multi-modality and multi-parametric modeling.
Wei Lise et al. The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of... 2019 Sep
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.
