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
Identifying non-adult attention-deficit/hyperactivity disorder individuals using a stacked machine learning algorithm using administrative data population registers in a universal healthcare system.
David Roche et al. JCPP Adv 2024 4(1) e12193
Evaluating ChatGPT's accuracy in providing screening mammography recommendations among older women: Artificial Intelligence and cancer communication.
Dejana Braithwaite et al. J Am Geriatr Soc 2024
Mortality risk prediction for primary appendiceal cancer.
Nolan M Winicki et al. Surgery 2024
Postmenopausal endometrial non-benign lesion risk classification through a clinical parameter-based machine learning model.
Jin Lai et al. Comput Biol Med 2024 172108243
Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features.
Giulio Del Corso et al. J Imaging Inform Med 2024
Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy.
Wenying Qiao et al. J Hepatocell Carcinoma 2024 11509-523
Construction of a predictive model for bone metastasis from first primary lung adenocarcinoma within 3 cm based on machine learning algorithm: a retrospective study.
Yu Zhang et al. PeerJ 2024 12e17098
Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging.
Clerimar Paulo Bragança et al. Diagnostics (Basel) 2024 14(5)
Validated, Quantitative, Machine Learning-Generated Neurologic Assessment of Multiple Sclerosis Using a Mobile Application.
Sharon Stoll et al. Int J MS Care 2024 26(2) 69-74
Deep learning-based fully automated grading system for dry eye disease severity.
Seonghwan Kim et al. PLoS One 2024 19(3) e0299776
Prediction of cognitive impairment using higher order item response theory and machine learning models.
Lihua Yao et al. Front Psychiatry 2024 141297952
Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis.
Benjamin A Goldstein et al. Am J Kidney Dis 2024
Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review.
Xiaojun Xu et al. J Educ Eval Health Prof 2024 216
Analysis and evaluation of explainable artificial intelligence on suicide risk assessment.
Hao Tang et al. Sci Rep 2024 14(1) 6163
Promoting Physical Activity Among Workers for Better Mental Health: An mHealth Intervention With Deep Learning.
Kazuhiro Watanabe et al. J UOEH 2024 46(1) 119-122
Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study.
Elham Hatef et al. JMIR Form Res 2024 8e54732
[Mobile health in primary care. New challenges in the development of solutions to promote physical activity and well-being].
Francesc Alòs et al. Aten Primaria 2024 56(8) 102900
Transformative potential of Artificial Intelligence on healthcare and research in Africa.
Moses J Bockarie et al. Int J Infect Dis 2024 107011
An Arrhythmia classification approach via deep learning using single-lead ECG without QRS wave detection.
Liong-Rung Liu et al. Heliyon 2024 10(5) e27200
Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.
Ben Li et al. JAMA Netw Open 2024 7(3) e242350
Automated valvular heart disease detection using heart sound with a deep learning algorithm.
Zihan Jiang et al. Int J Cardiol Heart Vasc 2024 51101368
Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.
Lianhua Liu et al. Front Endocrinol (Lausanne) 2024 151320335
A Machine Learning Approach to Predict HIV Viral Load Hotspots in Kenya Using Real-World Data.
Nancy Kagendi et al. Health Data Sci 2024 30019
Machine Learning Predictive Model for Septic Shock in Acute Pancreatitis with Sepsis.
Yiqin Xia et al. J Inflamm Res 2024 171443-1452
Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.
Qiu-Yan Yu et al. Front Big Data 2024 71291196
Artificial intelligence in imaging in the first trimester of pregnancy: a systematic review.
Emma Umans et al. Fetal Diagn Ther 2024
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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