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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 04/08/2021

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

Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning.
Greenbury Sam F et al. Scientific reports 2021 11(1) 7178

A Data Science Approach to Analyze the Association of Socioeconomic and Environmental Conditions With Disparities in Pediatric Surgery.
Akbilgic Oguz et al. Frontiers in pediatrics 2021 9620848

Cancer

An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy.
Perincheri Sudhir et al. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2021

Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive that categorizes a prostate biopsy whole-slide image as either "Suspicious" or "Not Suspicious" for prostatic adenocarcinoma.

Perspectives in pathomics in head and neck cancer.
Classe Marion et al. Current opinion in oncology 2021

Current applications of deep-learning in neuro-oncological MRI.
Zegers C M L et al. Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) 2021 83161-173

Artificial intelligence in assessment of hepatocellular carcinoma treatment response.
Spieler Bradley et al. Abdominal radiology (New York) 2021

Artificial Intelligence in Magnetic Resonance Imaging-based Prostate Cancer Diagnosis: Where Do We Stand in 2021?
Suarez-Ibarrola Rodrigo et al. European urology focus 2021

Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.
Castaldo Rossana et al. Journal of medical Internet research 2021 23(4) e22394

Development of a machine learning model for the prediction of nodal metastasis in early T classification oral squamous cell carcinoma: A SEER-based population study.
Kwak Min Seob et al. Head & neck 2021

Chronic Disease

Predicting unplanned medical visits among patients with diabetes: translation from machine learning to clinical implementation.
Selya Arielle et al. BMC medical informatics and decision making 2021 21(1) 111

Screening for Early-Stage Alzheimer's Disease Using Optimized Feature Sets and Machine Learning.
Kleiman Michael J et al. Journal of Alzheimer's disease : JAD 2021

Remote Physical Frailty Monitoring-The Application of Deep Learning-Based Image Processing in Tele-Health.
Zahiri Mohsen et al. IEEE access : practical innovations, open solutions 2020 8219391-219399

Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.
Chen Qingyu et al. Journal of the American Medical Informatics Association : JAMIA 2021

General Practice

The Way of the Future: Personalizing Treatment Plans Through Technology.
Liefaard Marte C et al. American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting 2021 411-12

How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
E Wu et al, Nature Medicine, April 5, 2021

A comprehensive overview of medical AI devices approved by the US Food and Drug Administration sheds new light on limitations of evaluation that can mask vulnerabilities of devices when they are deployed on patients.

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?
Mawdsley Emma et al. Journal of neuropsychology 2021

Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study.
Kim Ho Heon et al. JMIR mHealth and uHealth 2021 9(3) e22183

Digital Mental Health Challenges and the Horizon Ahead for Solutions.
Balcombe Luke et al. JMIR mental health 2021 8(3) e26811

Centralizing environmental datasets to support (inter)national chronic disease research: Values, challenges, and recommendations.
Brook Jeffrey R et al. Environmental epidemiology (Philadelphia, Pa.) 2021 5(1) e129

Data-Driven Surveillance: Effective Collection, Integration, and Interpretation of Data to Support Decision Making.
Dórea Fernanda C et al. Frontiers in veterinary science 2021 8633977

An increasing number of new data sources, including many not originally collected for health purposes, are now being used for epidemiological inference and contextualization. Combining evidence from multiple data sources presents significant challenges, but discussions around this subject often confuse issues of data access and privacy, with the actual technical challenges of data integration and interoperability.

Evaluation of machine learning algorithms for health and wellness applications: A tutorial.
Tohka Jussi et al. Computers in biology and medicine 2021 132104324

An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers.
Yu Hui et al. Movement ecology 2021 9(1) 15

A Framework for Criteria-Based Selection and Processing of Fast Healthcare Interoperability Resources (FHIR) Data for Statistical Analysis: Design and Implementation Study.
Gruendner Julian et al. JMIR medical informatics 2021 9(4) e25645

Task-specific information outperforms surveillance-style big data in predictive analytics.
Bjerre-Nielsen Andreas et al. Proceedings of the National Academy of Sciences of the United States of America 2021 118(14)

Heart, Lung, Blood and Sleep Diseases

Artificial Intelligence in Hypertension: Seeing Through a Glass Darkly.
Padmanabhan Sandosh et al. Circulation research 2021 128(7) 1100-1118

Detecting the Early Infarct Core on Non-Contrast CT Images with a Deep Learning Residual Network.
Pan Jiawei et al. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 2021 30(6) 105752

Machine learning compared with rule-in/rule-out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations.
Björkelund Anders et al. Journal of the American College of Emergency Physicians open 2021 2(2) e12363

Mortality Prediction of Patients With Cardiovascular Disease Using Medical Claims Data Under Artificial Intelligence Architectures: Validation Study.
Tran Linh et al. JMIR medical informatics 2021 9(4) e25000

Artificial Intelligence to Assist in Exclusion of Coronary Atherosclerosis During CCTA Evaluation of Chest Pain in the Emergency Department: Preparing an Application for Real-world Use.
White Richard D et al. Journal of digital imaging 2021

Infectious Diseases

Machine Learning Applied to Spanish Clinical Laboratory Data for COVID-19 Outcome Prediction: Model Development and Validation.
Domínguez-Olmedo Juan L et al. Journal of medical Internet research 2021

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.
Chen Chaojin et al. Respiratory research 2021 22(1) 94

Prediction of risk of acquiring urinary tract infection during hospital stay based on machine-learning: A retrospective cohort study.
Møller Jens Kjølseth et al. PloS one 2021 16(3) e0248636

Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy.
Ma Haozhong et al. Journal of microbiology (Seoul, Korea) 2021

COVID-19 Insights Partnership: Leveraging Big Data from the Department of Veterans Affairs and Supercomputers at the Department of Energy under the Public Health Authority.
Ramoni Rachel et al. Journal of the American Medical Informatics Association : JAMIA 2021

Accuracy of documented administration times for intravenous antimicrobial drugs and impact on dosing decisions.
Roydhouse Stephanie A et al. British journal of clinical pharmacology 2021

Reproductive Health

The prediction of preeclampsia: the way forward.
Myatt Leslie et al. American journal of obstetrics and gynecology 2021

Future studies should include women in both low- and high-resource settings and employ social media and novel methods for data collection and analysis, including machine learning and artificial intelligence. The goal is to identify the pathophysiology underlying differing preeclampsia phenotypes, their successful prediction with the design, and the implementation of phenotype-specific therapies.


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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