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
May Artificial Intelligence Influence Future Pediatric Research?-The Case of ChatGPT.
Antonio Corsello et al. Children (Basel) 2023 10(4)
Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation.
Michael A Mooney et al. Clin Psychol Sci 2023 11(3) 458-475
Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward?
Antonio Z Gimeno-García et al. Cancers (Basel) 2023 15(8)
Deep Learning Empowers Endoscopic Detection and Polyps Classification: A Multiple-Hospital Study.
Ming-Hung Shen et al. Diagnostics (Basel) 2023 13(8)
Incremental value of radiomics with machine learning to the existing prognostic models for predicting outcome in renal cell carcinoma.
Jiajun Xing et al. Front Oncol 2023 131036734
Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma.
Laura Alaimo et al. Ann Surg Oncol 2023
Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors.
Alissa Michel et al. Breast Cancer Res Treat 2023
Artificial Intelligence and laboratory data in rheumatic diseases.
Paola Galozzi et al. Clin Chim Acta 2023 117388
AI and ML ethics, Law, Diversity, and Global Impact.
Katherine Drabiak et al. Br J Radiol 2023 20220934
Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.
Xunliang Li et al. Ren Fail 2023 45(1) 2212790
Development and validation of a prediction equation for body fat percentage from measured BMI: a supervised machine learning approach.
Shiming Xu et al. Sci Rep 2023 13(1) 8010
Digital mental health: challenges and next steps.
Katharine A Smith et al. BMJ Ment Health 2023 26(1)
Relationship between a daily injury risk estimation feedback (I-REF) based on machine learning techniques and actual injury risk in athletics (track and field): protocol for a prospective cohort study over an athletics season.
Pierre-Eddy Dandrieux et al. BMJ Open 2023 13(5) e069423
Artificial Intelligence in Diagnosis of Oral Diseases: A Systematic Review.
Shaul Hameed Kolarkodi et al. J Contemp Dent Pract 2023 24(1) 61-68
Ensemble machine learning methods in screening electronic health records: A scoping review.
Christophe At Stevens et al. Digit Health 2023 920552076231173225
The use of artificial intelligence for automating or semi-automating biomedical literature analyses: a scoping review.
Álisson Oliveira Dos Santos et al. J Biomed Inform 2023 104389
Informatics and data science approaches address significant public health problems.
Suzanne Bakken et al. J Am Med Inform Assoc 2023 30(6) 1009-1010
Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome.
Sarawuth Limprasert et al. Healthc Inform Res 2023 29(2) 120-131
Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study.
Tongtong Yang et al. Brain Sci 2023 13(4)
Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury.
Meng-Pan Li et al. Eur Spine J 2023
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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