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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 07/29/2021

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

Predictive modelling of hypoxic ischaemic encephalopathy risk following perinatal asphyxia.
Mooney Catherine et al. Heliyon 2021 7(7) e07411

Neonatal mortality prediction with routinely collected data: a machine learning approach.
Batista André F M et al. BMC pediatrics 2021 21(1) 322

Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas.

Cancer

Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies.
Syer Tom et al. Cancers 2021 13(13)

Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis.
Cancian Pierandrea et al. Cancers 2021 13(13)

A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer.
Sun Peng et al. EBioMedicine 2021 70103492

Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography.
Li Dan et al. Abdominal radiology (New York) 2021

Chronic Disease

A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome.
Ocagli Honoria et al. International journal of environmental research and public health 2021 18(13)

Cortical thickness distinguishes between major depression and schizophrenia in adolescents.
Zhou Zheyi et al. BMC psychiatry 2021 21(1) 361

Ethical, Legal and Social Issues (ELSI)

Privacy-preserving data sharing via probabilistic modeling.
Jälkö Joonas et al. Patterns (New York, N.Y.) 2021 2(7) 100271

Differential privacy allows quantifying privacy loss resulting from accession of sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modeling.

General Practice

The perceptions of medical physicists towards relevance and impact of artificial intelligence.
Santos Josilene C et al. Physical and engineering sciences in medicine 2021

Machine learning for initial insulin estimation in hospitalized patients.
Nguyen Minh et al. Journal of the American Medical Informatics Association : JAMIA 2021

ImputEHR: A Visualization Tool of Imputation for the Prediction of Biomedical Data.
Zhou Yi-Hui et al. Frontiers in genetics 2021 12691274

The future of acute and emergency care.
Newcombe Virginia et al. Future healthcare journal 2021 8(2) e230-e236

Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey.
Antes Alison L et al. BMC medical informatics and decision making 2021 21(1) 221

Healthcare is expected to increasingly integrate technologies enabled by artificial intelligence (AI) into patient care. Understanding perceptions of these tools is essential to successful development and adoption. This exploratory study gauged participants' level of openness, concern, and perceived benefit associated with AI-driven healthcare technologies.

Heart, Lung, Blood and Sleep Diseases

Emerging role of artificial intelligence in stroke imaging.
Corrias Giuseppe et al. Expert review of neurotherapeutics 2021 1-10

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy.
Smole Tim et al. Computers in biology and medicine 2021 135104648

Respiratory Event Detection during Sleep Using Electrocardiogram and Respiratory Related Signals: Using Polysomnogram and Patch-Type Wearable Device Data.
Yeo Minsoo et al. IEEE journal of biomedical and health informatics 2021 PP

RobustSleepNet: Transfer learning for automated sleep staging at scale.
Guillot Antoine et al. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2021 PP

Infectious Diseases

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.
Chen Shi et al. Frontiers in public health 2021 9661615

Chest X-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: an individual patient data meta-analysis of diagnostic accuracy.
Tavaziva Gamuchirai et al. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2021


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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