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
Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.
Zihan Qu et al. J Gastroenterol Hepatol 2024
Development and validation of a random survival forest model for predicting long-term survival of early-stage young breast cancer patients based on the SEER database and an external validation cohort.
Lin-Wei Li et al. Am J Cancer Res 2024 14(4) 1609-1621
Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review.
Lea Sacca et al. J Clin Med 2024 13(9)
Impact of Transfer Learning Using Local Data on Performance of a Deep Learning Model for Screening Mammography.
James J J Condon et al. Radiol Artif Intell 2024 e230383
Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.
Yash Shashank Vakilna et al. Epilepsia 2024
Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning.
Wenxun Lin et al. Arthritis Res Ther 2024 26(1) 92
Artificial intelligence in epilepsy - applications and pathways to the clinic.
Alfredo Lucas et al. Nat Rev Neurol 2024
Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera.
Anna-Maria Kubin et al. Ann Med 2024 56(1) 2352018
Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis.
D Y Chow et al. J Dent Res 2024 220345241237448
Intensive longitudinal assessment following index trauma to predict development of PTSD using machine learning.
Adam Horwitz et al. J Anxiety Disord 2024 104102876
Predicting blood transfusion following traumatic injury using machine learning models: A systematic review and narrative synthesis.
William Oakley et al. J Trauma Acute Care Surg 2024
Redefining Healthcare With Artificial Intelligence (AI): The Contributions of ChatGPT, Gemini, and Co-pilot.
Anas Alhur et al. Cureus 2024 16(4) e57795
Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins.
Alexandre Vallée et al. J Med Internet Res 2024 26e50204
Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review.
Line Farah et al. J Med Internet Res 2024 26e51514
Artificial intelligence in the clinical laboratory.
Hanjing Hou et al. Clin Chim Acta 2024 559119724
AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential.
Malek Elhaddad et al. Cureus 2024 16(4) e57728
Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden.
Josline Adhiambo Otieno et al. PLoS One 2024 19(5) e0303287
Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.
Nicola Fink et al. Diagnostics (Basel) 2024 14(9)
Predictors of Disease Progression and Adverse Clinical Outcomes in Patients with Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.
Mahmoud Salem et al. Am J Cardiol 2024
Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm.
Navid Nemati et al. Diagnostics (Basel) 2024 14(9)
Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke.
Ahmad A Abujaber et al. BMC Neurol 2024 24(1) 156
Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.
Anjana G et al. J Crit Care 2024 83154815
The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.
Yuefei Li et al. BMC Infect Dis 2024 24(1) 474
Data mining and machine learning in HIV infection risk research: An overview and recommendations.
Qiwei Ge et al. Artif Intell Med 2024 153102887
Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.
Yue Wu et al. Front Pharmacol 2024 151366529
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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