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
Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning-Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers.
Aviv Y Landau et al. JMIR formative research 2023 7e40194
The Deterioration Risk Index: Developing and Piloting a Machine Learning Algorithm to Reduce Pediatric Inpatient Deterioration.
Laura O H Rust et al. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 2023
Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases.
Omeed Moaven et al. Journal of the American College of Surgeons 2023
Deep learning-based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia.
Xinyi Zhang et al. Cancer medicine 2023
Machine learning to predict overall short-term mortality in cutaneous melanoma.
C Cozzolino et al. Discover. Oncology 2023 14(1) 13
Novel, alternative splicing signature to detect lymph node metastasis in prostate adenocarcinoma with machine learning.
Ping Xie et al. Frontiers in oncology 2023 121084403
Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review.
Maanya Rajasree Katta et al. Journal of cancer research and clinical oncology 2023
Clinical Implementation of a Combined AI and NLP Quality Assurance Program for Pulmonary Nodule Detection in the ED Setting.
Joseph J Cavallo et al. Journal of the American College of Radiology : JACR 2023
Artificial intelligence technology for myopia challenges: A review.
Juzhao Zhang et al. Frontiers in cell and developmental biology 2023 111124005
Agreement of a Novel Artificial Intelligence Software with Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma.
Sujani Shroff et al. Journal of glaucoma 2023
Tele-Glaucoma Using a New Smartphone-based Tool for Visual Field Assessment.
Elisabeth Grau et al. Journal of glaucoma 2023
A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction.
Kun Zhou et al. Frontiers in aging neuroscience 2023 141073909
Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple sclerosis.
Aleide Hoeijmakers et al. Scientific reports 2023 13(1) 1871
Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study.
Henry David Jeffry Hogg et al. BMJ open 2023 13(2) e069443
Review of Artificial Intelligence-Based Signal Processing in Dialysis: Challenges for Machine-Embedded and Complementary Applications.
Lena Scherer et al. Advances in kidney disease and health 2023 30(1) 40-46
Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.
Ashir Javeed et al. Journal of medical systems 2023 47(1) 17
Prediction of early-wheelchair dependence in multiple system atrophy based on machine learning algorithm: A prospective cohort study.
Lingyu Zhang et al. Clinical parkinsonism & related disorders 2023 8100183
Chronic kidney disease prediction based on machine learning algorithms.
Md Ariful Islam et al. Journal of pathology informatics 2023 14100189
Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia.
Malo Gaubert et al. Frontiers in psychiatry 2023 131010273
Web-Based Delirium Prevention Application for Long-Term Care Facilities.
Mina Park et al. Journal of the American Medical Directors Association 2023
Improved Parkinsonian tremor quantification based on automatic label modification and SVM with RBF kernel.
Yumin Li et al. Physiological measurement 2023
Interpretable machine learning for dementia: A systematic review.
Sophie A Martin et al. Alzheimer's & dementia : the journal of the Alzheimer's Association 2023
Early detection of depression using a conversational AI bot: A non-clinical trial.
Payam Kaywan et al. PloS one 2023 18(2) e0279743
Artificial intelligence technologies and compassion in healthcare: A systematic scoping review.
Elizabeth Morrow et al. Frontiers in psychology 2023 13971044
Competencies for the Use of Artificial Intelligence-Based Tools by Health care Professionals.
Regina G Russell et al. Academic medicine : journal of the Association of American Medical Colleges 2023
Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis.
Yen Hsu et al. Computers, informatics, nursing : CIN 2023
Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice.
Kevin Lopez et al. PloS one 2023 18(2) e0280251
Performance of Multiple Imputation Using Modern Machine Learning Methods in Electronic Health Records Data.
Kylie Getz et al. Epidemiology (Cambridge, Mass.) 2023 34(2) 206-215
Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery.
Constance Boissin et al. Scientific reports 2023 13(1) 1794
Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study.
Nuo Han et al. Journal of medical Internet research 2023 25e41823
MIDRC CRP10 AI interface - an integrated tool for exploring, testing and visualization of AI models.
Naveena Gorre et al. Physics in medicine and biology 2023
Fairness in the prediction of acute postoperative pain using machine learning models.
Anis Davoudi et al. Frontiers in digital health 2023 4970281
Application of digital pathology and machine learning in the liver, kidney and lung diseases.
Benjamin Wu et al. Journal of pathology informatics 2023 14100184
Research on emergency management of global public health emergencies driven by digital technology: A bibliometric analysis.
Chao Wen et al. Frontiers in public health 2023 101100401
Impact of a machine learning algorithm on time to palliative care in a primary care population: protocol for a stepped-wedge pragmatic randomized trial.
Ethan P Heinzen et al. BMC palliative care 2023 22(1) 9
Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling.
Mingfu Nuo et al. Journal of medical Internet research 2023 25e42856
Diagnostic performance of a machine-learning algorithm (Asthma/COPD Differentiation Classification; AC/DC) tool versus primary care physicians and pulmonologists in asthma, COPD and ACO.
Janwillem W H Kocks et al. The journal of allergy and clinical immunology. In practice 2023
Applications of artificial intelligence and machine learning in heart failure.
Tauben Averbuch et al. European heart journal. Digital health 2023 3(2) 311-322
Efficient screening for severe aortic valve stenosis using understandable artificial intelligence: a prospective diagnostic accuracy study.
Hisaki Makimoto et al. European heart journal. Digital health 2023 3(2) 141-152
Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review.
P M Croon et al. European heart journal. Digital health 2023 3(3) 415-425
Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.
Jian Huang et al. Frontiers in public health 2023 101086339
Diagnosis of obstructive sleep apnea in children based on the XGBoost algorithm using nocturnal heart rate and blood oxygen feature.
Pengfei Ye et al. American journal of otolaryngology 2023 44(2) 103714
Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.
Nhung Nghiem et al. Health economics review 2023 13(1) 9
Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts.
Yikuan Li et al. European heart journal. Digital health 2023 3(4) 535-547
Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study.
Ying Yang et al. International journal of medical informatics 2023 172105007
An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients.
Muhammad Zia Rahman et al. Computers in biology and medicine 2023 154106583
Determinants and prediction of Chlamydia trachomatis re-testing and re-infection within 1 year among heterosexuals with chlamydia attending a sexual health clinic.
Xianglong Xu et al. Frontiers in public health 2023 101031372
OB HUB: Remote Electronic Fetal Monitoring Surveillance.
Deb Lowery et al. MCN. The American journal of maternal child nursing 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:May 02, 2024
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