About Non-Genomics Precision Health Scan
This update features emerging roles of big data science, machine learning, and predictive analytics across the life span. The scan focus on various conditions including, birth defects, newborn screening, reproductive health, childhood diseases, cancer, chronic diseases, medication, family health history, guidelines and recommendations. The sweep also includes news, reviews, commentaries, tools and database. View Data Selection Criteria
Non-Genomics Precision Health Scan
The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study.
Xu Yuyun et al. Journal of magnetic resonance imaging : JMRI 2021 Feb
Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning.
Keswani Rajesh N et al. Endoscopy international open 2021 Feb 9(2) E233-E238
The Million-Dollar Question: Can Empowerment Help People Take Steps to Prevent, Conquer, and Control Cancer?
L Richardson, CDC Cancer Blog, February 2021
A Novel Machine Learning Algorithm Predicts Dementia With Lewy Bodies Versus Parkinson's Disease Dementia Based on Clinical and Neuropsychological Scores.
Bougea Anastasia et al. Journal of geriatric psychiatry and neurology 2021 Feb 891988721993556
Identifying patterns of health care utilisation among physical elder abuse victims using Medicare data and legally adjudicated cases: protocol for case-control study using data linkage and machine learning.
Rosen Tony et al. BMJ open 2021 Feb 11(2) e044768
Predicting Depression From Hearing Loss Using Machine Learning.
Crowson Matthew G et al. Ear and hearing 2021 Jan
Quantification of abdominal fat from computed tomography using deep learning and its association with electronic health records in an academic biobank.
MacLean Matthew T et al. Journal of the American Medical Informatics Association : JAMIA 2021 Feb
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.
Lee Ellen E et al. Biological psychiatry. Cognitive neuroscience and neuroimaging 2021 Feb
Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records.
Zheng Hua et al. Drugs 2021 Feb
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data
M Ravaut et al, NPJ Digital Medicine, February 12, 2021
From clinical decision support to clinical reasoning support systems.
van Baalen Sophie et al. Journal of evaluation in clinical practice 2021 Feb
Mortality Prediction in Cerebral Hemorrhage Patients Using Machine Learning Algorithms in Intensive Care Units.
Nie Ximing et al. Frontiers in neurology 2020 11610531
Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables.
Sherratt Freddie et al. Sensors (Basel, Switzerland) 2021 Feb 21(4)
Future perspective of heart failure care: benefits and bottlenecks of artificial intelligence and eHealth.
Amin Hesam et al. Future cardiology 2021 Feb
Early Detection of Severe Functional Impairment Among Adolescents With Major Depression Using Logistic Classifier.
Chiu I-Ming et al. Frontiers in public health 2020 8622007
A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.
Zhao Qin-Yu et al. Frontiers in medicine 2020 7637434
An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.
Ding Ning et al. BioMed research international 2021 20216638919
Epistemic Responsibilities in the COVID-19 Pandemic: Is a Digital Infosphere a Friend or a Foe?
Marko Curkovic et al. Journal of biomedical informatics 2021 Feb 103709
Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases.
Lu Jiaying et al. Environmental science and pollution research international 2021 Feb
Disclaimer: Articles listed in Non-Genomics Precision Health Scan are selected by the CDC Office of Genomics and Precision Public Health 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.
- Page last reviewed:Feb 1, 2024
- Page last updated:Mar 28, 2024
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