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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 09/30/2021

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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Non-Genomics Precision Health Scan

Birth Defects and Child Health

Novel Application of Machine Learning Algorithms and Model-Agnostic Methods to Identify Factors Influencing Childhood Blood Lead Levels.
Liu Xiaochi et al. Environmental science & technology 2021

In this study, we utilized recent advances in machine learning to assess multiple algorithms and identify the most optimal model for predicting childhood blood Pb levels (BLL) using Broken Hill children's (<5 years of age) data (n = 23,749) from 1991 to 2015, combined with demographic, socio-economic, and environmental influencing factors. We applied model-agnostic methods to interpret the most optimal model, investigating different environmental and human factors influencing childhood BLL.

Cancer

Interpretability-Based Multimodal Convolutional Neural Networks for Skin Lesion Diagnosis.
Wang Sutong et al. IEEE transactions on cybernetics 2021 PP

Surveillance Strategy for Barcelona Clinic Liver Cancer B Hepatocellular Carcinoma Achieving Complete Response: An Individualized Risk-Based Machine Learning Study.
Chen Qi-Feng et al. Frontiers in bioengineering and biotechnology 2021 9667641

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma.
Nassiri Nima et al. European urology focus 2021

An Algorithm to Personalize Nerve Sparing in Men with Unilateral High-Risk Prostate Cancer.
Martini Alberto et al. The Journal of urology 2021 101097JU0000000000002205

Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting.
Giavina-Bianchi Mara et al. PloS one 2021 16(9) e0257006

A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses.
Zhang Di et al. Medical science monitor : international medical journal of experimental and clinical research 2021 27e931957

Analysis of Population Differences in Digital Conversations About Cancer Clinical Trials: Advanced Data Mining and Extraction Study.
Perez Edith A et al. JMIR cancer 2021 7(3) e25621

Deep Learning-Based CT Imaging in Diagnosing Myeloma and Its Prognosis Evaluation.
Wang Jinzhou et al. Journal of healthcare engineering 2021 20215436793

Use of classifiers to optimise the identification and characterisation of metastatic breast cancer in a nationwide administrative registry.
Valachis Antonis et al. Acta oncologica (Stockholm, Sweden) 2021 1-7

Radiomic modeling to predict risk of vertebral compression fracture after stereotactic body radiation therapy for spinal metastases.
Gui Chengcheng et al. Journal of neurosurgery. Spine 2021 1-9

Chronic Disease

[Attitude of patients to possible telemedicine in ophthalmology : Survey by questionnaire in patients with glaucoma].
Zwingelberg Sarah B et al. Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft 2021

Assessment of Artificial Intelligence Automatic Multiple Sclerosis Lesion Delineation Tool for Clinical Use.
Hindsholm Amalie Monberg et al. Clinical neuroradiology 2021

Highlighting psychological pain avoidance and decision-making bias as key predictors of suicide attempt in major depressive disorder-A novel investigative approach using machine learning.
Ji Xinlei et al. Journal of clinical psychology 2021

A Deep Learning Model to Predict Knee Osteoarthritis Based on Nonimage Longitudinal Medical Record.
Ningrum Dina Nur Anggraini et al. Journal of multidisciplinary healthcare 2021 142477-2485

Evaluation of a New Neural Network Classifier for Diabetic Retinopathy.
Katz Or et al. Journal of diabetes science and technology 2021 19322968211042665

Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France.
Haneef Romana et al. Archives of public health = Archives belges de sante publique 2021 79(1) 168

Detection of ataxia in low disability MS patients by hybrid convolutional neural networks based on images of plantar pressure distribution.
Balgetir Ferhat et al. Multiple sclerosis and related disorders 2021 56103261

Using machine learning methods to predict hepatic encephalopathy in cirrhotic patients with unbalanced data.
Yang Hong et al. Computer methods and programs in biomedicine 2021 211106420

Effects of an Artificial Intelligence-Assisted Health Program on Workers With Neck/Shoulder Pain/Stiffness and Low Back Pain: Randomized Controlled Trial.
Anan Tomomi et al. JMIR mHealth and uHealth 2021 9(9) e27535

Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.
Zou Winnie Y et al. Hepatology communications 2021

Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease - What the Clinician Needs to Know.
Chen David et al. Journal of Crohn's & colitis 2021

General Practice

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review.
Chan Victor C H et al. Applied ergonomics 2021 98103574

Stakeholders' perspectives on the future of artificial intelligence in radiology: a scoping review.
Yang Ling et al. European radiology 2021

Trends and Patterns in the Public Awareness of Palliative Care, Euthanasia, and End-of-Life Decisions in 3 Central European Countries Using Big Data Analysis From Google: Retrospective Analysis.
Huemer Matthias et al. Journal of medical Internet research 2021 23(9) e28635

Medical student knowledge and critical appraisal of machine learning: a multicentre international cross-sectional study.
Blacketer Charlotte et al. Internal medicine journal 2021 51(9) 1539-1542

Predicting outcomes of psychotherapy for depression with electronic health record data.
Coley R Yates et al. Journal of affective disorders reports 2021 6100198

Machine learning for identification of frailty in Canadian primary care practices.
Aponte-Hao Sylvia et al. International journal of population data science 2021 6(1) 1650

Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods:: Framework, Strategies, and Role of the Physician.
Jha Abhinav K et al. PET clinics 2021 16(4) 493-511

Knowledge and Attitudes Toward an Artificial Intelligence-Based Fidelity Measurement in Community Cognitive Behavioral Therapy Supervision.
Creed Torrey A et al. Administration and policy in mental health 2021

Equitable Implementation of Artificial Intelligence in Medical Imaging: What Can be Learned from Implementation Science?
Yousefi Nooraie Reza et al. PET clinics 2021 16(4) 643-653

Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions.
Parashar Gaurav et al. SN computer science 2021 2(6) 461

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression.
DeSouza Danielle D et al. Frontiers in psychiatry 2021 12719125

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review.
Herman Daniel S et al. Clinical chemistry 2021

Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.
Wang Xiaofeng et al. Frontiers in public health 2021 9697850

The purpose of this paper is to predict mental health of medical workers based on machine learning by 32 factors. We collected the 32 factors of 5,108 Chinese medical workers through questionnaire survey, and the results of Self-reporting Inventory was applied to characterize mental health. In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers.

Clinical decision support for severe trauma patients : Machine Learning based definition of a bundle of care for Hemorrhagic Shock and Traumatic Brain Injury.
Lang E et al. The journal of trauma and acute care surgery 2021

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.
Moorman Liza Prudente et al. Applied clinical informatics 2021 12(4) 888-896

Precision Public Health Campaign: Delivering Persuasive Messages to Relevant Segments Through Targeted Advertisements on Social Media.
An Jisun et al. JMIR formative research 2021 5(9) e22313

Although established marketing techniques have been applied to design more effective health campaigns, more often than not, the same message is broadcasted to large populations, irrespective of unique characteristics. As individual digital device use has increased, so have individual digital footprints, creating potential opportunities for targeted digital health interventions. We propose a novel precision public health campaign framework to structure and standardize the process of designing and delivering tailored health messages to target particular population segments using social media-targeted advertising tools.

Heart, Lung, Blood and Sleep Diseases

Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review.
Zhao Yuan et al. American journal of preventive medicine 2021 61(4) 596-605

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.
Hardacre Conor Joseph et al. The British journal of radiology 2021 20210332

Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature.
Hsu Yu-Ching et al. Journal of sleep research 2021 e13487

Construction of a Risk Prediction Model for Hospital-Acquired Pulmonary Embolism in Hospitalized Patients.
Hou Lengchen et al. Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis 2021 2710760296211040868

Machine Learning Identifies Clinical Parameters to Predict Mortality in Patients Undergoing Transcatheter Mitral Valve Repair.
Zweck Elric et al. JACC. Cardiovascular interventions 2021 14(18) 2027-2036

Automatic Assessment of Mitral Regurgitation Severity Using the Mask R-CNN Algorithm with Color Doppler Echocardiography Images.
Zhang Qinglu et al. Computational and mathematical methods in medicine 2021 20212602688

Arteriovenous Fistula Flow Dysfunction Surveillance: Early Detection Using Pulse Radar Sensor and Machine Learning Classification.
Chen Cheng-Hsu et al. Biosensors 2021 11(9)


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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