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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 08/05/2021

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

Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation.
Sazawal Sunil et al. Journal of global health 2021 1104044

Cancer

Artificial Intelligence and Polyp Detection in Colonoscopy: Use of a Single Neural Network to Achieve Rapid Polyp Localization for Clinical Use.
Li James Weiquan et al. Journal of gastroenterology and hepatology 2021

Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for Glioma Segmentation from Multi-modal MRI Volumetric Data.
Ali Muhammad Junaid et al. Journal of digital imaging 2021

The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions.
Lazar Daniela Cornelia et al. Gastroenterology report 2021 9(3) 185-204

Predicting Colorectal Cancer Recurrence and Patient Survival Using Supervised Machine Learning Approach: A South African Population-Based Study.
Achilonu Okechinyere J et al. Frontiers in public health 2021 9694306

Predictive Radiomic Models for the Chemotherapy Response in Non-Small-Cell Lung Cancer based on Computerized-Tomography Images.
Chang Runsheng et al. Frontiers in oncology 2021 11646190

Chronic Disease

Machine Learning Algorithm Using Electronic Chart-Derived Data to Predict Delirium After Elderly Hip Fracture Surgeries: A Retrospective Case-Control Study.
Zhao Hong et al. Frontiers in surgery 2021 8634629

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic.
Brinkmann Benjamin H et al. Frontiers in neurology 2021 12690404

This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems

Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.
Darvishi Sonia et al. Journal of preventive medicine and hygiene 2021 62(1) E192-E199

Random forest approach for determining risk prediction and predictive factors of type 2 diabetes: large-scale health check-up data in Japan.
Ooka Tadao et al. BMJ nutrition, prevention & health 2021 4(1) 140-148

Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease.
Chen Guihua et al. Frontiers in bioengineering and biotechnology 2021 9635764

Ethical, Legal and Social Issues (ELSI)

A Call for an Ethics and Governance Action Plan to Harness the Power of Artificial Intelligence and Digitalization in Nephrology.
Ho Calvin Wai-Loon et al. Seminars in nephrology 2021 41(3) 282-293

Blockchain for Increased Trust in Virtual Health Care: Proof-of-Concept Study.
Hasselgren Anton et al. Journal of medical Internet research 2021 23(7) e28496

Role of Public Health Ethics for Responsible Use of Artificial Intelligence Technologies.
Bhattacharya Sudip et al. Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine 2021 46(2) 178-181

Guidelines for Conducting Ethical Artificial Intelligence Research in Neurology: A Systematic Approach for Clinicians and Researchers.
Chiang Sharon et al. Neurology 2021

Digital Technologies and Data Science as Health Enablers: An Outline of Appealing Promises and Compelling Ethical, Legal, and Social Challenges.
Cordeiro João V et al. Frontiers in medicine 2021 8647897

General Practice

Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries.
Schmid William et al. Journal of neural engineering 2021

In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss possibilities that recent advances in machine learning can bring to bear in providing mTBI patients with timely diagnosis and treatment

Analyzing Patient Secure Messages Using a Fast Health Care Interoperability Resources (FIHR)-Based Data Model: Development and Topic Modeling Study.
De Amrita et al. Journal of medical Internet research 2021 23(7) e26770

A Systematic Approach for Integrating Machine Learning Models into the Clinic.
Savjani Ricky et al. Radiology. Imaging cancer 2021 3(4) e219014

A Systematic Review of The Impact of Commercial Aircraft Activity on Air Quality Near Airports.
Riley Karie et al. City and environment interactions 2021 11

Application of Comprehensive Artificial intelligence Retinal Expert (CARE) system: a national real-world evidence study.
Lin Duoru et al. The Lancet. Digital health 2021 3(8) e486-e495

Applicability of machine learning techniques in food intake assessment: A systematic review.
Oliveira Chaves Larissa et al. Critical reviews in food science and nutrition 2021 1-18

Detecting suicidal risk using MMPI-2 based on machine learning algorithm.
Kim Sunhae et al. Scientific reports 2021 11(1) 15310

Hospital Length of Stay Prediction Methods: A Systematic Review.
Lequertier Vincent et al. Medical care 2021

Effects of environmental factors on health risks by using machine learning.
Ma Wangrong et al. Work (Reading, Mass.) 2021

The impact of industry cluster and environmental policies on residents' health risk evaluation using big data.
Chen Limin et al. Work (Reading, Mass.) 2021

Personalised, population and planetary nutrition for precision health.
Martínez-González Miguel A et al. BMJ nutrition, prevention & health 2021 4(1) 355-358

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing.
Squiers John J et al. Journal of vascular surgery 2021

Data-driven identification of complex disease phenotypes.
Strauss Markus J et al. Journal of the Royal Society, Interface 2021 18(180) 20201040

Decision-support tools via mobile devices to improve quality of care in primary healthcare settings.
Agarwal Smisha et al. The Cochrane database of systematic reviews 2021 7CD012944

Big data-based analysis to characterise and identify variations in the type of Primary Care visits before and during COVID in Catalonia.
Lopez Segui Francesc et al. Journal of medical Internet research 2021

Ten lessons learnt: scaling and transitioning one of the largest mobile health communication programmes in the world to a national government.
Chamberlain Sara et al. BMJ global health 2021 6(Suppl 5)

Socio-demographic determinants of physical activity and app usage from smartphone data.
Pontin Francesca et al. Social science & medicine (1982) 2021 284114235

The increasing ubiquity of smartphones provides a potential new data source to capture physical activity behaviours. Though not designed as a research tool, these secondary data have the potential to capture a large population over a more extensive spatial area and with longer temporality than current methods afford. This paper uses one such secondary data source from a commercial app designed to incentivise activity

Heart, Lung, Blood and Sleep Diseases

Predicting the survivals and favorable neurologic outcomes after targeted temperature management by artificial neural networks.
Chiu Wei-Ting et al. Journal of the Formosan Medical Association = Taiwan yi zhi 2021

County-level phenomapping to identify disparities in cardiovascular outcomes: An unsupervised clustering analysis: Short title: Unsupervised clustering of counties and risk of cardiovascular mortality.
Segar Matthew W et al. American journal of preventive cardiology 2021 4100118

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing.
Lineback Christina M et al. Frontiers in neurology 2021 12649521

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.
Imura Takeshi et al. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 2021 30(10) 106011

Interpretability of time-series deep learning models: a study in cardiovascular patients admitted to Intensive Care Unit.
Gandin Ilaria et al. Journal of biomedical informatics 2021 103876

Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.
Farahani Nasibeh Zanjirani et al. Proceedings. IEEE International Conference on Bioinformatics and Biomedicine 2021 20201932-1937

Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison.
Ali Md Mamun et al. Computers in biology and medicine 2021 136104672

Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review.
Zhang Lu et al. European journal of hybrid imaging 2021 5(1) 14

Infectious Diseases

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning.
Cromwell Elizabeth A et al. PLoS neglected tropical diseases 2021 15(7) e0008824

Diverse experts' perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study.
Nichol Ariadne A et al. BMJ open 2021 11(7) e052287

Reproductive Health

Assessing exposure to Kilkari: a big data analysis of a large maternal mobile messaging service across 13 states in India.
Bashingwa Jean Juste Harrisson et al. BMJ global health 2021 6(Suppl 5)


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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