Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content

Genomics & Precision Health Database|Non-Genomics Precision Health Update Archive|Public Health Genomics and Precision Health Knowledge Base (PHGKB) Published on 11/03/2022

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

Archived Editions

Search Precision Health database

Visit CDC Office of Public Health Genomics website

Birth Defects and Child Health

Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents.
Kushwaha Savitesh et al. Computer methods and programs in biomedicine 2022 226107180

Cancer

Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international, multicentre analysis.
Pfob André et al. European journal of cancer (Oxford, England : 1990) 2022 1771-14

Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.
Li Suli et al. Mediators of inflammation 2022 20221734327

Imaging standardisation in metastatic colorectal cancer: A joint EORTC-ESOI-ESGAR expert consensus recommendation.
Unterrainer Marcus et al. European journal of cancer (Oxford, England : 1990) 2022 176193-206

Survival analysis of localized prostate cancer with deep learning.
Dai Xin et al. Scientific reports 2022 12(1) 17821

Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database.
Fan Guoxin et al. Global spine journal 2022 21925682221134049

An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer.
Omobolaji Alabi Rasheed et al. International journal of medical informatics 2022 168104896

A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer.
Yuan Yufei et al. BioMed research international 2022 20228501819

Development and validation of a machine learning model to predict venous thromboembolism among hospitalized cancer patients.
Meng Lingqi et al. Asia-Pacific journal of oncology nursing 2022 9(12) 100128

Machine learning models for predicting survival in patients with ampullary adenocarcinoma.
Huang Tao et al. Asia-Pacific journal of oncology nursing 2022 9(12) 100141

Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach.
Bassel Atheer et al. Diagnostics (Basel, Switzerland) 2022 12(10)

The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.
Grenier Philippe A et al. Diagnostics (Basel, Switzerland) 2022 12(10)

Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review.
Kawka Michal et al. Translational gastroenterology and hepatology 2022 741

Use Case Evaluation and Digital Workflow of Breast Cancer Care by Artificial Intelligence and Blockchain Technology Application.
Griewing Sebastian et al. Healthcare (Basel, Switzerland) 2022 10(10)

A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques.
Alshohoumi Fatma et al. Healthcare (Basel, Switzerland) 2022 10(10)

Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types.
Rigaud Bastien et al. Cancers 2022 14(20)

Chronic Disease

Prediction of renal damage in children with IgA vasculitis based on machine learning.
Wang Jinjuan et al. Medicine 2022 101(42) e31135

Advanced imaging and Crohn's disease: An overview of clinical application and the added value of artificial intelligence.
Grassi Giovanni et al. European journal of radiology 2022 157110551

Predicting Clinical Remission of Chronic Urticaria Using Random Survival Forests: Machine Learning Applied to Real-World Data.
Pivneva Irina et al. Dermatology and therapy 2022

General Practice

Dynamic prediction of life-threatening events for patients in intensive care unit.
Hu Jiang et al. BMC medical informatics and decision making 2022 22(1) 276

Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov.
Wang Anran et al. International journal of environmental research and public health 2022 19(20)

Development and Evaluation of Machine Learning-Based High-Cost Prediction Model Using Health Check-Up Data by the National Health Insurance Service of Korea.
Choi Yeongah et al. International journal of environmental research and public health 2022 19(20)

Artificial Intelligence in Dermatology: Challenges and Perspectives.
Liopyris Konstantinos et al. Dermatology and therapy 2022

Artificial intelligence-based methods for fusion of electronic health records and imaging data.
Mohsen Farida et al. Scientific reports 2022 12(1) 17981

Heart, Lung, Blood and Sleep Diseases

Device agnostic AI-based analysis of ambulatory ECG recordings.
Kennedy Alan et al. Journal of electrocardiology 2022 74154-157

Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study.
Da Ros Valerio et al. Neuroradiology 2022

Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation.
O'Brien Megan K et al. IEEE journal of translational engineering in health and medicine 2022 102100711

Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms.
Garavand Ali et al. Journal of healthcare engineering 2022 20225359540

Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography.
O'Driscoll Jamie M et al. European heart journal open 2022 2(5) oeac059

Deep Learning for Estimating Lung Capacity on Chest Radiographs Predicts Survival in Idiopathic Pulmonary Fibrosis.
Kim Hyungjin et al. Radiology 2022 220292

Heart disease detection based on internet of things data using linear quadratic discriminant analysis and a deep graph convolutional neural network.
Saikumar K et al. Frontiers in computational neuroscience 2022 16964686

Automatic identification of early ischemic lesions on non-contrast CT with deep learning approach.
Sahoo Prasan Kumar et al. Scientific reports 2022 12(1) 18054

Rapid lipid-laden plaque identification in intravascular optical coherence tomography imaging based on time-series deep learning.
Rico-Jimenez Jose J et al. Journal of biomedical optics 2022 27(10)

Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials.
Oikonomou Evangelos K et al. The Lancet. Digital health 2022 4(11) e796-e805

Predictors of Adherence to Stroke Prevention in the BALKAN-AF Study: A Machine-Learning Approach.
Koziel-Siolkowska Monika et al. TH open : companion journal to thrombosis and haemostasis 2022 6(3) e283-e290

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review.
Huang Jian-Dong et al. Sensors (Basel, Switzerland) 2022 22(20)

A Deep Learning Framework for Automatic Sleep Apnea Classification Based on Empirical Mode Decomposition Derived from Single-Lead Electrocardiogram.
Setiawan Febryan et al. Life (Basel, Switzerland) 2022 12(10)

A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease.
Shen Tao et al. Journal of clinical medicine 2022 11(20)

Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing.
González-Juanatey Carlos et al. Journal of clinical medicine 2022 11(20)

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review.
Subudhi Asit et al. Diagnostics (Basel, Switzerland) 2022 12(10)

Infectious Diseases

Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.
Keshavamurthy Ravikiran et al. One health (Amsterdam, Netherlands) 2022 15100439

Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis.
Tang Rui et al. International journal of antimicrobial agents 2022 106684

Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios.
Haas Oliver et al. Frontiers in reproductive health 2022 3756405

Evaluation of machine learning algorithms for predicting direct-acting antiviral treatment failure among patients with chronic hepatitis C infection.
Park Haesuk et al. Scientific reports 2022 12(1) 18094

Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.
Cabrera Maritza et al. Tropical medicine and infectious disease 2022 7(10)

Reproductive Health

A review on deep-learning algorithms for fetal ultrasound-image analysis.
Fiorentino Maria Chiara et al. Medical image analysis 2022 83102629

Challenges of Utilizing Medical Big Data in Reproductive Health Research.
Dong Tianyu et al. Frontiers in reproductive health 2022 4800760


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.
TOP