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
The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study.
Junjie Zeng et al. Sci Rep 2024 14(1) 6609
Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.
Hui Shen et al. Radiol Med 2024
From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring.
Mariana Michelle Ramírez-Mejía et al. World J Gastroenterol 2024 30(7) 631-635
Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.
Shayeste Alinia et al. Heliyon 2024 10(6) e27854
Prediction of tumor lysis syndrome in childhood acute lymphoblastic leukemia based on machine learning models: a retrospective study.
Yao Xiao et al. Front Oncol 2024 141337295
Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.
Ryuichiro Yagi et al. Nat Commun 2024 15(1) 2536
Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis.
Fabiano Papaiz et al. BMC Med Inform Decis Mak 2024 24(1) 80
Artificial intelligence insights into osteoporosis: assessing ChatGPT's information quality and readability.
Yakup Erden et al. Arch Osteoporos 2024 19(1) 17
Ethics of artificial intelligence in medicine.
Julian Savulescu et al. Singapore Med J 2024 65(3) 150-158
A paradigm shift?-On the ethics of medical large language models.
Thomas Grote et al. Bioethics 2024
Artificial intelligence (AI) or augmented intelligence? How big data and AI are transforming healthcare: Challenges and opportunities.
K Moodley et al. S Afr Med J 2024 114(1) 22-26
Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.
Zhen Ling Teo et al. Cell Rep Med 2024 5(3) 101481
Clinical use cases in artificial intelligence: current trends and future opportunities.
Cher Heng Tan et al. Singapore Med J 2024 65(3) 183-185
Artificial Intelligence Simulation of Adolescents' Responses to Vaping-Prevention Messages.
Paschal Sheeran et al. JAMA Pediatr 2024
Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome.
Mitchel A Molenaar et al. Eur Heart J Digit Health 2024 5(2) 170-182
Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.
Jakob Weiss et al. Ann Intern Med 2024
Emerging artificial intelligence-aided diagnosis and management methods for ischemic strokes and vascular occlusions: A comprehensive review.
G A U R I Parvathy et al. World Neurosurg X 2024 22100303
Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures.
Conghui Wei et al. Sci Rep 2024 14(1) 6901
Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study.
Yafei Wu et al. Psychogeriatrics 2024
Development and Validation of Machine Learning Algorithms to Predict 1-Year Ischemic Stroke and Bleeding Events in Patients with Atrial Fibrillation and Cancer.
Bang Truong et al. Cardiovasc Toxicol 2024
Empowering child health: Harnessing machine learning to predict acute respiratory infections in Ethiopian under-fives using demographic and health survey insights.
Mulugeta Hayelom Kalayou et al. BMC Infect Dis 2024 24(1) 338
Explainable machine learning for early predicting treatment failure risk among patients with TB-diabetes comorbidity.
An-Zhou Peng et al. Sci Rep 2024 14(1) 6814
Machine learning-enabled maternal risk assessment for women with pre-eclampsia (the PIERS-ML model): a modelling study.
Tünde Montgomery-Csobán et al. Lancet Digit Health 2024 6(4) e238-e250
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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