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
Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics.
Hansa Bhargava et al. J Med Internet Res 2024 26e49022
Telemedicine-Enhanced Lung Cancer Screening Using Mobile Computed Tomography Unit with Remote Artificial Intelligence Assistance in Underserved Communities: Initial Results of a Population Cohort Study in Western China.
Wenjuan Tao et al. Telemed J E Health 2024
Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.
Ruishan Liu et al. Cell Rep Med 2024 101444
Exploring the landscape of AI-assisted decision-making in head and neck cancer treatment: a comparative analysis of NCCN guidelines and ChatGPT responses.
Filippo Marchi et al. Eur Arch Otorhinolaryngol 2024
A novel machine learning prediction model for metastasis in breast cancer.
Huan Li et al. Cancer Rep (Hoboken) 2024 7(3) e2006
Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.
Jie Bao et al. Insights Imaging 2024 15(1) 68
Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice.
B Bersu Ozcan et al. J Breast Imaging 2024 5(3) 248-257
Developmental Prediction of Poststroke Patients in Activities of Daily Living by Using Tree-Structured Parzen Estimator-Optimized Stacking Ensemble Approaches.
Pei-Hua Lin et al. IEEE J Biomed Health Inform 2024 PP
Machine learning based predictive model of Type 2 diabetes complications using Malaysian National Diabetes Registry: A study protocol.
Mohamad Zulfikrie Abas et al. J Public Health Res 2024 13(1) 22799036241231786
Prediction models for earlier stages of chronic kidney disease.
Mackenzie Alexiuk et al. Curr Opin Nephrol Hypertens 2024
Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals.
Elaheh Moradi et al. Alzheimers Res Ther 2024 16(1) 46
Comparison of Endoscopic and Artificial Intelligence Diagnoses for Predicting the Histological Healing of Ulcerative Colitis in a Real-World Clinical Setting.
Teppei Omori et al. Crohns Colitis 360 2024 6(1) otae005
Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings.
Queralt Miró Catalina et al. Sci Rep 2024 14(1) 5199
Integrating artificial intelligence into healthcare systems: more than just the algorithm.
Jethro C C Kwong et al. NPJ Digit Med 2024 7(1) 52
Artificial intelligence in clinical practice: A look at ChatGPT.
Jiawen Deng et al. Cleve Clin J Med 2024 91(3) 173-180
Development, validation, and transportability of several machine-learned, non-exercise-based VO prediction models for older adults.
Benjamin T Schumacher et al. J Sport Health Sci 2024
Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource.
Jingsong Luo et al. Psychol Res Behav Manag 2024 17691-703
Towards quality management of artificial intelligence systems for medical applications.
Lorenzo Mercolli et al. Z Med Phys 2024
Predicting early-onset COPD risk in adults aged 20-50 using electronic health records and machine learning.
Guanglei Liu et al. PeerJ 2024 12e16950
Cloud-Based Machine Learning Platform to Predict Clinical Outcomes at Home for Patients With Cardiovascular Conditions Discharged From Hospital: Clinical Trial.
Phillip C Yang et al. JMIR Cardio 2024 8e45130
Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.
Jingjing Fan et al. Stress Health 2024 e3386
Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children.
Nader Shaikh et al. JAMA Pediatr 2024
The prediction of in-hospital mortality in elderly patients with sepsis-associated acute kidney injury utilizing machine learning models.
Jie Tang et al. Heliyon 2024 10(4) e26570
Applying Machine Learning to Blood Count Data Predicts Sepsis with ICU Admission.
Daniel Steinbach et al. Clin Chem 2024 70(3) 506-515
A machine learning-based risk score for prediction of infective endocarditis among patients with Staphylococcus aureus bacteraemia - The SABIER score.
Christopher Koon-Chi Lai et al. J Infect Dis 2024
Automated Machine Learning versus Expert-Designed Models in Ocular Toxoplasmosis: Detection and Lesion Localization Using Fundus Images.
Daniel Milad et al. Ocul Immunol Inflamm 2024 1-7
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
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