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
Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.
Campbell J Peter et al. Pediatrics 2021
Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2).
Reed Robert A et al. Frontiers in pediatrics 2021 8585868
Tumor microenvironment and the role of artificial intelligence in breast cancer detection and prognosis.
Malherbe Kathryn et al. The American journal of pathology 2021
Development and Validation of Machine Learning-based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts.
Chen Kezhong et al. Clinical cancer research : an official journal of the American Association for Cancer Research 2021
Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data.
Rossi Lorenzo A et al. Journal of surgical oncology 2021
Novel artificial intelligence machine learning approaches to precisely predict survival and site-specific recurrence in cervical cancer: A multi-institutional study.
Guo Chenyan et al. Translational oncology 2021 14(5) 101032
Artificial Intelligence in NASH Histology: Human Teaches a Machine for the Machine to help Humans.
Noureddin Mazen et al. Hepatology (Baltimore, Md.) 2021
Leveraging electronic health records data to predict multiple sclerosis disease activity.
Ahuja Yuri et al. Annals of clinical and translational neurology 2021
A Risk Prediction Model Based on Machine Learning for Cognitive Impairment Among Chinese Community-Dwelling Elderly People With Normal Cognition: Development and Validation Study.
Hu Mingyue et al. Journal of medical Internet research 2021 23(2) e20298
Clinical applications of artificial intelligence and machine learning-based methods in inflammatory bowel disease.
Cohen-Mekelburg Shirley et al. Journal of gastroenterology and hepatology 2021 36(2) 279-285
Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network.
Al-Khuzaie Fanar E K et al. Applied bionics and biomechanics 2021 20216690539
Comparison of an Artificial Intelligence–Enabled Patient Decision Aid vs Educational Material on Decision Quality, Shared Decision-Making, Patient Experience, and Functional Outcomes in Adults With Knee Osteoarthritis
A Randomized Clinical Trial
P Jayakumar et al, JAMA Network Open, February 2021
Health Information Privacy Laws in the Digital Age: HIPAA Doesn't Apply.
Theodos Kim et al. Perspectives in health information management 2021 18(Winter) 1l
Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS).
Romero-Brufau Santiago et al. Journal of the American Medical Informatics Association : JAMIA 2021
Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence.
Park Seong Ho et al. Korean journal of radiology 2021 22(3) 442-453
Sex-Specific Risk Profiles for Suicide Among Persons with Substance Use Disorders in Denmark.
Adams Rachel Sayko et al. Addiction (Abingdon, England) 2021
Predicting Self-Rated Health Across the Life Course: Health Equity Insights from Machine Learning Models.
Clark Cheryl R et al. Journal of general internal medicine 2021
Artificial Intelligence in Medical Sensors for Clinical Decisions.
Haick Hossam et al. ACS nano 2021
Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks.
Sammani Arjan et al. NPJ digital medicine 2021 4(1) 37
Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning.
Wang Chen et al. Frontiers in cardiovascular medicine 2021 8614204
The Emerging Role of Quantification of Imaging for Assessing the Severity and Disease Activity of Emphysema, Airway Disease, and Interstitial Lung Disease.
Goldin Jonathan Gerald et al. Respiration; international review of thoracic diseases 2021 1-14
Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis.
Kaplan Alan et al. The journal of allergy and clinical immunology. In practice 2021
Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.
Park Jung In et al. Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing 2021
Finding New Meaning in Everyday Electrocardiograms-Leveraging Deep Learning to Expand Our Diagnostic Toolkit.
Tison Geoffrey H et al. JAMA cardiology 2021
Trends of pulmonary fungal infections from 2013-2019: an AI-based real-world observational study in Guangzhou, China.
Li Zhengtu et al. Emerging microbes & infections 2021 1-31
Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19.
Wu Zhiyuan et al. European journal of radiology 2021 137109602
Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study.
Hochman Eldar et al. Depression and anxiety 2021
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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