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Genomics & Precision Health Database|Non-Genomics Precision Health Update Archive|Public Health Genomics and Precision Health Knowledge Base (PHGKB) Published on 10/06/2022

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

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Birth Defects and Child Health

Generalizability and Bias in a Deep Learning Pediatric Bone Age Prediction Model Using Hand Radiographs.
Beheshtian Elham et al. Radiology 2022 220505

Artificial intelligence for assessing the severity of microtia via deep convolutional neural networks.
Wang Dawei et al. Frontiers in surgery 2022 9929110

Metabolic syndrome screening in adolescents: New scores AI_METS based on artificial intelligence techniques.
Benmohammed Karima et al. Nutrition, metabolism, and cardiovascular diseases : NMCD 2022

Cancer

Prediction of prognosis and survival of patients with gastric cancer by a weighted improved random forest model: an application of machine learning in medicine.
Xu Cheng et al. Archives of medical science : AMS 2022 18(5) 1208-1220

An inception-based deep multiparametric net to classify clinical significance MRI regions of prostate cancer.
Gutierrez Yesid Alfonso et al. Physics in medicine and biology 2022

Artificial Intelligence in Oncological Hybrid Imaging.
Feuerecker Benedikt et al. RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin 2022

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system.
Haq Amin Ul et al. Journal of ambient intelligence and humanized computing 2022 1-12

Differentiation of malignant from benign pleural effusions based on artificial intelligence.
Wang Sufei et al. Thorax 2022

Chronic Disease

Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods.
Yu Zehao et al. BMC medical informatics and decision making 2022 22(Suppl 3) 255

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study.
Machado-Fragua Marcos D et al. BMC medicine 2022 20(1) 334

An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III.
Sibley Krista et al. Journal of Parkinson's disease 2022

The Impact of a Digital Artificial Intelligence System on the Monitoring and Self-management of Nonmotor Symptoms in People With Parkinson Disease: Proposal for a Phase 1 Implementation Study.
Meinert Edward et al. JMIR research protocols 2022 11(9) e40317

Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning.
Gao Zhiyuan et al. The British journal of ophthalmology 2022

On the reliability of deep learning-based classification for Alzheimer's disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation.
Song Yeong-Hun et al. Frontiers in neuroscience 2022 16851871

Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population - a machine learning approach.
Gagnon-Sanschagrin Patrick et al. BMC psychiatry 2022 22(1) 630

Ethical, Legal and Social Issues (ELSI)

The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States.
Milam M E et al. Clinical radiology 2022

General Practice

Applying ensemble machine learning models to predict individual response to a digitally delivered worry postponement intervention.
Gyorda Joseph A et al. Journal of affective disorders 2022

Openness and Transparency in the Evaluation of Bias in Artificial Intelligence.
Larson David B et al. Radiology 2022 222263

Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods.
Jung Daniel et al. Journal of applied gerontology : the official journal of the Southern Gerontological Society 2022 7334648221129548

Clinlabomics: leveraging clinical laboratory data by data mining strategies.
Wen Xiaoxia et al. BMC bioinformatics 2022 23(1) 387

Tackling bias in AI health datasets through the STANDING Together initiative.
Ganapathi Shaswath et al. Nature medicine 2022

Tree-Based Algorithms and Association Rule Mining for Predicting Patients' Neurological Outcomes After First-Aid Treatment for an Out-of-Hospital Cardiac Arrest During COVID-19 Pandemic: Application of Data Mining.
Lin Wei-Chun et al. International journal of general medicine 2022 157395-7405

Leveraging electronic health records for data science: common pitfalls and how to avoid them.
Sauer Christopher M et al. The Lancet. Digital health 2022

Artificial intelligence in clinical endoscopy: Insights in the field of videomics.
Paderno Alberto et al. Frontiers in surgery 2022 9933297

Surgomics: personalized prediction of morbidity, mortality and long-term outcome in surgery using machine learning on multimodal data.
Wagner Martin et al. Surgical endoscopy 2022

An introduction to machine learning for classification and prediction.
Black Jason E et al. Family practice 2022

On the Use of Bayesian Artificial Intelligence for Hypothesis Generation in Psychiatry.
Briganti Giovanni et al. Psychiatria Danubina 2022 34(Suppl 8) 201-206

Effect of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity: A Randomized Clinical Trial.
Popp Collin J et al. JAMA network open 2022 5(9) e2233760

Machine learning and artificial intelligence: applications in healthcare epidemiology.
Hamilton Alisa J et al. Antimicrobial stewardship & healthcare epidemiology : ASHE 2022 1(1) e28

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
Daye Dania et al. Radiology 2022 305(1) E62

Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache.
Vandenbussche Nicolas et al. The journal of headache and pain 2022 23(1) 129

Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study.
Hong Julian C et al. BMC bioinformatics 2022 23(Suppl 12) 408

Epidemiology Evidence for Health Effects of 150 per- and Polyfluoroalkyl Substances: A Systematic Evidence Map.
Radke Elizabeth G et al. Environmental health perspectives 2022 130(9) 96003

Epitweetr: Early warning of public health threats using Twitter data.
Espinosa Laura et al. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2022 27(39)

Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review.
Plana Deborah et al. JAMA network open 2022 5(9) e2233946

Three simple steps to improve the interpretability of EEG-SVM studies.
Joucla Coralie et al. Journal of neurophysiology 2022

Heart, Lung, Blood and Sleep Diseases

Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing: validation in a low-risk to intermediate-risk outpatient clinic cohort.
Eurlings Casper G M J et al. BMJ open 2022 12(9) e055170

Artificial intelligence-enabled electrocardiography identifies severe dyscalcemias and has prognostic value.
Lin Chin et al. Clinica chimica acta; international journal of clinical chemistry 2022

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis.
Zeng Minyan et al. Frontiers in neurology 2022 13945813

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases.
Cai Dabei et al. Frontiers in cardiovascular medicine 2022 9964894

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization.
Johri Amer M et al. Computers in biology and medicine 2022 150106018

Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial.
Noseworthy Peter A et al. Lancet (London, England) 2022

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.
Ferreira-Santos Daniela et al. Journal of medical Internet research 2022 24(9) e39452

Infectious Diseases

Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: A meta-analysis of 26 vaccine regimens.
Huang Yunda et al. EBioMedicine 2022 84104271

Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms.
Wang Xin et al. Communications medicine 2022 2119

Reproductive Health

Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm.
Zhou Mingjuan et al. Frontiers in medicine 2022 9811890


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.
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