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
Interpreting deep learning models for epileptic seizure detection on EEG signals.
Gabeff Valentin et al. Artificial intelligence in medicine 2021 117102084
Strabismus and Artificial Intelligence App: Optimizing Diagnostic and Accuracy.
de Figueiredo Laura Alves et al. Translational vision science & technology 2021 10(7) 22
Machine learning model for predicting excessive muscle loss during neoadjuvant chemoradiotherapy in oesophageal cancer.
Yoon Han Gyul et al. Journal of cachexia, sarcopenia and muscle 2021
A review of AI and Data Science support for cancer management.
Parimbelli E et al. Artificial intelligence in medicine 2021 117102111
Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer.
Osman Mohamed Hosny et al. Cancer research and treatment 2021
Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer.
Kimura Koichiro et al. European radiology 2021
The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.
Huang Danju et al. Technology in cancer research & treatment 2021 2015330338211016386
A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.
Chen Yonghe et al. Frontiers in oncology 2021 11675458
Three-dimensional U-Net Convolutional Neural Network for Detection and Segmentation of Intracranial Metastases.
Rudie Jeffrey D et al. Radiology. Artificial intelligence 2021 3(3) e200204
A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.
Zheng Junjiong et al. Kidney international 2021
Using real-word data to evaluate the effects of broadening eligibility criteria in oncology trials.
Sanz-Garcia Enrique et al. Cancer cell 2021 39(6) 750-752
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques.
Tautan Alexandra-Maria et al. Artificial intelligence in medicine 2021 117102081
Using a Stacked Autoencoder for Mobility and Fall Risk Assessment via Time-Frequency Representations of the Timed Up and Go Test.
Chen Shih-Hai et al. Frontiers in physiology 2021 12668350
Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression.
De Brouwer Edward et al. Computer methods and programs in biomedicine 2021 208106180
Electronic Medical Record Risk Modeling of Cardiovascular Outcomes Among Patients with Type 2 Diabetes.
Hong Dongzhe et al. Diabetes therapy : research, treatment and education of diabetes and related disorders 2021
Social Media Big Data: The Good, The Bad, and the Ugly (Un)truths.
Chew Alton M K et al. Frontiers in big data 2021 4623794
Leveraging big data for pattern recognition of socio-demographic and climatic factors in correlation with eye disorders in Telangana State, India.
Alalawi Amna et al. Indian journal of ophthalmology 2021 69(7) 1894-1900
[Prediction of intensive care unit readmission for critically ill patients based on ensemble learning].
Lin Y et al. Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences 2021 53(3) 566-572
A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission.
Mišic Velibor V et al. NPJ digital medicine 2021 4(1) 98
A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future.
Kyrimi Evangelia et al. Artificial intelligence in medicine 2021 117102108
Interpretability of Machine Learning Solutions in Public Healthcare: The CRISP-ML Approach.
Kolyshkina Inna et al. Frontiers in big data 2021 4660206
The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition.
Limketkai Berkeley N et al. Current surgery reports 2021 9(7) 20
Predictors of tooth loss: A machine learning approach.
Elani Hawazin W et al. PloS one 2021 16(6) e0252873
Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review.
Asan Onur et al. JMIR human factors 2021 8(2) e28236
Using Machine Learning to Predict Severe Hypoglycemia in Hospital.
Fralick Michael et al. Diabetes, obesity & metabolism 2021
Realising the full potential of data-enabled trials in the UK: a call for action.
Sydes Matthew R et al. BMJ open 2021 11(6) e043906
Applications of Artificial Intelligence and Machine Learning in Disasters and Public Health Emergencies.
Lu Sally et al. Disaster medicine and public health preparedness 2021 1-8
Development and validation of a scoring system for mortality prediction and application of standardized W statistics to assess the performance of emergency departments.
Jeong Jinwoo et al. BMC emergency medicine 2021 21(1) 71
Analysis of Mental Health Disease Trends Using BeGraph Software in Spanish Health Care Centers: Case Study.
Góngora Alonso Susel et al. JMIR medical informatics 2021 9(6) e15527
Quantification of Motor Function Post-stroke using Novel Combination of Wearable Inertial and Mechanomyographic Sensors.
Formstone Lewis et al. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2021 PP
Novel Digital Technologies for Blood Pressure Monitoring and Hypertension Management.
Hare Allison J et al. Current cardiovascular risk reports 2021 15(8) 11
CT EvaLuation by ARtificial Intelligence For Atherosclerosis, Stenosis and Vascular MorphologY (CLARIFY): A Multi-center, international study.
Choi Andrew D et al. Journal of cardiovascular computed tomography 2021
A new approach for interpretability and reliability in clinical risk prediction: Acute coronary syndrome scenario.
Valente Francisco et al. Artificial intelligence in medicine 2021 117102113
Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution.
Asch Federico M et al. Circulation. Cardiovascular imaging 2021 14(6) e012293
Automated classification of coronary atherosclerotic plaque in optical frequency domain imaging based on deep learning.
Shibutani Hiroki et al. Atherosclerosis 2021 328100-105
Artificial Intelligence-Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population.
Kashou Anthony H et al. Mayo Clinic proceedings 2021
Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction.
Lee Woojoo et al. Scientific reports 2021 11(1) 12886
Enhanced Detection of Heart Valve Disease Using Integrated Artificial Intelligence at Scale.
O'Hair Daniel P et al. The Annals of thoracic surgery 2021
Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models.
Priya Sarv et al. Scientific reports 2021 11(1) 12686
Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.
Moor Michael et al. Frontiers in medicine 2021 8607952
Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events.
Miller Michele et al. JMIR public health and surveillance 2021 7(6) e27976
Toward the use of neural networks for influenza prediction at multiple spatial resolutions.
Aiken Emily L et al. Science advances 2021 7(25)
Detecting Suicide and Self-Harm Discussions Among Opioid Substance Users on Instagram Using Machine Learning.
Purushothaman Vidya et al. Frontiers in psychiatry 2021 12551296
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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