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
Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning.
Yiru Yang et al. J Perinat Med 2023
Epidemiology and a predictive model of prognosis index based on machine learning in primary breast lymphoma: Population-Based Study.
Yushuai Yu et al. JMIR Public Health Surveill 2023
Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients.
Jingkun Qu et al. J Clin Med 2023 12(9)
Application of Artificial Intelligence to the Diagnosis and Therapy of Nasopharyngeal Carcinoma.
Xinggang Yang et al. J Clin Med 2023 12(9)
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models.
Lennart Brocki et al. Cancers (Basel) 2023 15(9)
Predicting the survival of patients with pancreatic neuroendocrine neoplasms using deep learning: A study based on Surveillance, Epidemiology, and End Results database.
Chen Jiang et al. Cancer Med 2023
Artificial Intelligence and Predictive Modeling in Spinal Oncology: A Narrative Review.
Rene Harmen Kuijten et al. Int J Spine Surg 2023
Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study.
Ash Kieran Clift et al. BMJ 2023 381e073800
Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach.
Nazanin Mobini et al. Eur Radiol 2023
Application of machine learning algorithms to predict osteoporosis in postmenopausal women with type 2 diabetes mellitus.
X Wu et al. J Endocrinol Invest 2023
A machine learning model identifies patients in need of autoimmune disease testing using electronic health records.
Iain S Forrest et al. Nat Commun 2023 14(1) 2385
Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.
Joseph Mellor et al. Int J Med Inform 2023 175105072
Predictive biosignature of major depressive disorder derived from physiological measurements of outpatients using machine learning.
Nicolas Ricka et al. Sci Rep 2023 13(1) 6332
A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management.
Hong Pan et al. Front Med (Lausanne) 2023 101136653
Machine learning for the prediction of cognitive impairment in older adults.
Wanyue Li et al. Front Neurosci 2023 171158141
Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021.
Oumer Abdulkadir Ebrahim et al. Sci Rep 2023 13(1) 7779
Sarcopenia Prediction for Elderly People Using Machine Learning: A Case Study on Physical Activity.
Minje Seok et al. Healthcare (Basel) 2023 11(9)
The reporting of neuropsychiatric symptoms in electronic health records of individuals with Alzheimer's disease: a natural language processing study.
Willem S Eikelboom et al. Alzheimers Res Ther 2023 15(1) 94
Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives.
Alan G Fraser et al. Expert Rev Med Devices 2023 1-25
Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review.
Mohanad M Alsaleh et al. Int J Med Inform 2023 175105088
Editorial: Clinical validation of digital health technologies for personalized medicine.
Michelle Khine et al. Front Digit Health 2023 4831517
Combined use of gray matter volume and neuropsychological test performance for classification of individuals with bipolar I disorder via artificial neural network method.
Baris Metin et al. J Neural Transm (Vienna) 2023
Unsupervised learning for prognostic validity in patients with chronic pain in transdisciplinary pain care.
Irina A Strigo et al. Sci Rep 2023 13(1) 7581
Research utility and limitations of textual data in the National Violent Death Reporting System: a scoping review and recommendations.
Linh N Dang et al. Inj Epidemiol 2023 10(1) 23
Machine Learning and the Digital Measurement of Psychological Health.
Isaac R Galatzer-Levy et al. Annu Rev Clin Psychol 2023 19133-154
Efficacy versus Effectiveness in Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis: The Award Goes to Effectiveness.
Guillaume Herpe et al. Radiology 2023 223132
A machine learning approach for the diagnosis of obstructive sleep apnoea using oximetry, demographic and anthropometric data.
Zhou Hao Leong et al. Singapore Med J 2023
Machine learning-enhanced echocardiography for screening coronary artery disease.
Ying Guo et al. Biomed Eng Online 2023 22(1) 44
The feasibility of early detecting coronary artery disease using deep learning-based algorithm based on electrocardiography.
Panli Tang et al. Aging (Albany NY) 2023 15(9) 3524-3537
Deep learning-based measurement of echocardiographic data and its application in the diagnosis of sudden cardiac death.
Lu Zhang et al. Biotechnol Genet Eng Rev 2023 1-13
Using machine learning algorithms to identify chronic heart disease: National Health and Nutrition Examination Survey 2011-2018.
Xiaofei Chen et al. J Cardiovasc Med (Hagerstown) 2023
Multimodal fusion models for pulmonary embolism mortality prediction.
Noa Cahan et al. Sci Rep 2023 13(1) 7544
A prospective observational multicentric clinical trial to evaluate microscopic examination of acid-fast bacilli in sputum by artificial intelligence-based microscopy system.
Prashant Gupta et al. J Investig Med 2023 10815589231171402
Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study.
Rianne Kablan et al. Int J Med Inform 2023 175105090
Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.
Anton H van der Vegt et al. J Am Med Inform Assoc 2023
Chest Radiography of Tuberculosis: Determination of Activity using Deep Learning Algorithm.
Ye Ra Choi et al. Tuberc Respir Dis (Seoul) 2023
Application and Progress of Artificial Intelligence in Fetal Ultrasound.
Sushan Xiao et al. J Clin Med 2023 12(9)
Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.
Yequn Chen et al. Hypertens Res 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
- Page last updated:Apr 25, 2024
- Content source: