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
Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials.
Ghasemi Elham et al. Cognitive neurodynamics 2022 16(6) 1335-1349
Utilizing big data from electronic health records in pediatric clinical care.
Macias Charles G et al. Pediatric research 2022 1-8
An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation.
Moghadam Saeed Montazeri et al. The Lancet. Digital health 2022 4(12) e884-e892
Influence of artificial intelligence on the adenoma detection rate throughout the day.
Richter Rino et al. Digestive diseases (Basel, Switzerland) 2022
Application of an Interpretable Machine Learning Model to Predict Lymph Node Metastasis in Patients with Laryngeal Carcinoma.
Feng Menglong et al. Journal of oncology 2022 20226356399
Differentiation of clear cell and non-clear-cell renal cell carcinoma through CT-based Radiomics models and nomogram.
Cheng Delu et al. Current medical imaging 2022
A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms.
Al-Tam Riyadh M et al. Biomedicines 2022 10(11)
Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer.
Rabaan Ali A et al. Cancers 2022 14(22)
Artificial Intellgence in the Era of Precision Oncological Imaging.
Cellina Michaela et al. Technology in cancer research & treatment 2022 2115330338221141793
Artificial Intelligence for Evaluation of Thyroid Nodules: A Primer.
Tessler Franklin N et al. Thyroid : official journal of the American Thyroid Association 2022
The value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study.
Kudo Maíra Suzuka et al. Patient safety in surgery 2022 16(1) 36
Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions.
Foahom Gouabou Arthur Cartel et al. International journal of molecular sciences 2022 23(22)
Development of Predictive Models for Survival among Women with Breast Cancer in Malaysia.
Nik Ab Kadir Mohd Nasrullah et al. International journal of environmental research and public health 2022 19(22)
DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection.
Latif Ghazanfar et al. Diagnostics (Basel, Switzerland) 2022 12(11)
Assessment of performance of the machine learning-based breast cancer risk prediction models: a systematic review.
Gao Ying et al. JMIR public health and surveillance 2022
External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence.
Hsu William et al. JAMA network open 2022 5(11) e2242343
Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: a systematic review.
Huq Syed Mahmudul et al. Disability and rehabilitation. Assistive technology 2022 1-20
Predicting diabetic nephropathy in type 2 diabetic patients using machine learning algorithms.
Hosseini Sarkhosh Seyyed Mahdi et al. Journal of diabetes and metabolic disorders 2022 21(2) 1433-1441
Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients.
Kanda Eiichiro et al. Scientific reports 2022 12(1) 20012
The effectiveness of artificial intelligence-based automated grading and training system in education of manual detection of diabetic retinopathy.
Qian Xu et al. Frontiers in public health 2022 101025271
Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective.
Wu Yafei et al. BMC geriatrics 2022 22(1) 900
Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type.
Qin Yifan et al. International journal of environmental research and public health 2022 19(22)
Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling.
Austin Andrea M et al. BMC medical research methodology 2022 22(1) 300
Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure.
Divard Gillian et al. Communications medicine 2022 2(1) 150
Artificial Intelligence in Elderly Healthcare: A Scoping Review.
Ma Bingxin et al. Ageing research reviews 2022 101808
Application of Machine Learning in Epileptic Seizure Detection.
Tran Ly V et al. Diagnostics (Basel, Switzerland) 2022 12(11)
Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness.
Shin Juyoung et al. Journal of personalized medicine 2022 12(11)
Needs and views on healthy lifestyles for the prevention of dementia and the potential role for mobile health (mHealth) interventions in China: a qualitative study.
Zhang Jinxia et al. BMJ open 2022 12(11) e061111
Automated differential diagnosis of dementia syndromes using FDG PET and machine learning.
Perovnik Matej et al. Frontiers in aging neuroscience 2022 141005731
Predicting mortality risk in dialysis: Assessment of risk factors using traditional and advanced modeling techniques within the Monitoring Dialysis Outcomes initiative.
Chaudhuri Sheetal et al. Hemodialysis international. International Symposium on Home Hemodialysis 2022
Phenotypes of non-alcoholic fatty liver disease (NAFLD) and all-cause mortality: unsupervised machine learning analysis of NHANES III.
Carrillo-Larco Rodrigo M et al. BMJ open 2022 12(11) e067203
Adaptability of AI for safety evaluation in regulatory science: A case study of drug-induced liver injury.
Connor Skylar et al. Frontiers in artificial intelligence 2022 51034631
Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare-A Scoping Review of Reviews.
Prakash Sreenidhi et al. Journal of personalized medicine 2022 12(11)
Machine learning approaches for electronic health records phenotyping: a methodical review.
Yang Siyue et al. Journal of the American Medical Informatics Association : JAMIA 2022
Preparing the Next Generation of Clinicians for Practice Using Augmented and Artificial Intelligence.
Ghorbanifarajzadeh Mina et al. Compendium of continuing education in dentistry (Jamesburg, N.J. : 1995) 2022 43(10) e1-e4
Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.
Forman Evan M et al. Contemporary clinical trials 2022 107029
A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions.
Mavrogiorgou Argyro et al. Sensors (Basel, Switzerland) 2022 22(22)
Modern views of machine learning for precision psychiatry.
Chen Zhe Sage et al. Patterns (New York, N.Y.) 2022 3(11) 100602
Automatic assessment of calcified plaque and nodule by optical coherence tomography adopting deep learning model.
Chen Tao et al. The international journal of cardiovascular imaging 2022 38(11) 2501-2510
A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters.
Sayadi Mohammadjavad et al. Life (Basel, Switzerland) 2022 12(11)
Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening.
Chalkidou Anastasia et al. The Lancet. Digital health 2022 4(12) e899-e905
Mitigating the impact of biased artificial intelligence in emergency decision-making.
Adam Hammaad et al. Communications medicine 2022 2(1) 149
Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis.
Abdollahifard Saeed et al. Journal of neurointerventional surgery 2022
The Utility of Automated ASPECTS in Acute Ischemic Stroke for Intravenous Recombinant Tissue Plasminogen Activator (IV-rtPA) Therapy.
Shibata Soichiro et al. Neurology international 2022 14(4) 981-990
Machine learning approach in diagnosing Takotsubo cardiomyopathy: The role of the combined evaluation of atrial and ventricular strain, and parametric mapping.
Cau Riccardo et al. International journal of cardiology 2022
A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease.
Yan Jingjing et al. Computers in biology and medicine 2022 151(Pt A) 106300
Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease.
Cox Meredith et al. PloS one 2022 17(11) e0277507
Community-based participatory research application of an artificial intelligence-enhanced electrocardiogram for cardiovascular disease screening: A FAITH! Trial ancillary study.
Harmon David M et al. American journal of preventive cardiology 2022 12100431
Physicians and Machine-Learning Algorithm Performance in Predicting Left-Ventricular Systolic Dysfunction from a Standard 12-Lead-Electrocardiogram.
Golany Tomer et al. Journal of clinical medicine 2022 11(22)
Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging.
Szabo Liliana et al. Frontiers in cardiovascular medicine 2022 91016032
Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.
Kim Hyerim et al. International journal of environmental research and public health 2022 19(22)
Word2vec Word Embedding-Based Artificial Intelligence Model in the Triage of Patients with Suspected Diagnosis of Major Ischemic Stroke: A Feasibility Study.
Desai Antonio et al. International journal of environmental research and public health 2022 19(22)
Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.
Fuadah Yunendah Nur et al. Diagnostics (Basel, Switzerland) 2022 12(11)
Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.
Cui Liyuan et al. BioMed research international 2022 20222456550
The role of digital health in the cardiovascular learning healthcare system.
Maddula Ragasnehith et al. Frontiers in cardiovascular medicine 2022 91008575
Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study.
Kong Vungsovanreach et al. PeerJ 2022 10e14348
Risk Association of Liver Cancer and Hepatitis B with Tree Ensemble and Lifestyle Features.
Koh Eunji et al. International journal of environmental research and public health 2022 19(22)
Deep learning-based classification of infectious keratitis on slit-lamp images.
Zhang Zijun et al. Therapeutic advances in chronic disease 2022 1320406223221136071
Prediction of Adverse Outcomes in De Novo Hypertensive Disorders of Pregnancy: Development and Validation of Maternal and Neonatal Prognostic Models.
Chen Junjun et al. Healthcare (Basel, Switzerland) 2022 10(11)
Machine Learning Techniques Outperform Conventional Statistical Methods in the Prediction of High Risk QTc Prolongation Related to a Drug-Drug Interaction.
Van Laere Sven et al. Journal of medical systems 2022 46(12) 100
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
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