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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 11/18/2021

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

Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging.
Chougule Tanay et al. NMR in biomedicine 2021 e4647

Personalized application of machine learning algorithms to identify pediatric patients at risk for recurrent ureteropelvic junction obstruction after dismembered pyeloplasty.
Drysdale Erik et al. World journal of urology 2021

Cancer

Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model.
Yala Adam et al. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2021 JCO2101337

Accurate risk assessment is essential for the success of population screening programs in breast cancer. Models with high sensitivity and specificity would enable programs to target more elaborate screening efforts to high-risk populations, while minimizing overtreatment for the rest. Artificial intelligence (AI)-based risk models have demonstrated a significant advance over risk models used today in clinical practice. However, the responsible deployment of novel AI requires careful validation across diverse populations. To this end, we validate our AI-based model, Mirai, across globally diverse screening populations

Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry.
Wahid Kareem A et al. Clinical and translational radiation oncology 2021 326-14

Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma.
Hellwig Konstantin et al. Frontiers in oncology 2021 11734872

Prediction of Breast Cancer Recurrence Using a Deep Convolutional Neural Network Without Region-of-Interest Labeling.
Phan Nam Nhut et al. Frontiers in oncology 2021 11734015

Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.
van Eekelen Leander et al. Pathology 2021

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.
Ruini Cristel et al. Cancers 2021 13(21)

Scope of Artificial Intelligence in Gastrointestinal Oncology.
Goyal Hemant et al. Cancers 2021 13(21)

A Mass Spectrometry Imaging Based Approach for Prognosis Prediction in UICC Stage I/II Colon Cancer.
Martin Benedikt et al. Cancers 2021 13(21)

Segmentation of Gastric Computerized Tomography Images under Intelligent Algorithms in Evaluation of Efficacy of Decitabine Combined with Paclitaxel in Treatment of Gastric Cancer.
Ge Zhenghui et al. Journal of healthcare engineering 2021 20218023490

Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study.
Li Tao et al. Journal of healthcare engineering 2021 20215972962

Chronic Disease

Development and multicenter validation of FIB-6; a novel, machine-learning, simple bedside score to rule out liver cirrhosis and compensated advanced chronic liver disease in CHC patients.
Shiha G et al. Hepatology research : the official journal of the Japan Society of Hepatology 2021

The Utility of Smartphone-Based Artificial Intelligence Approaches for Diabetic Retinopathy: A Literature Review and Meta-Analysis.
Sheikh Aadil et al. Journal of current ophthalmology 2021 33(3) 219-226

GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes.
De Bois Maxime et al. Medical & biological engineering & computing 2021

Automatic migraine classification using artificial neural networks.
Sanchez-Sanchez Paola A et al. F1000Research 2021 9618

Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes.
Tena Félix et al. Sensors (Basel, Switzerland) 2021 21(21)

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI.
Fu Xiaoxuan et al. Journal of healthcare engineering 2021 20211834123

Ethical, Legal and Social Issues (ELSI)

A healthy debate: Exploring the views of medical doctors on the ethics of artificial intelligence.
Martinho Andreia et al. Artificial intelligence in medicine 2021 121102190

General Practice

The Promise for Reducing Healthcare Cost with Predictive Model: An Analysis with Quantized Evaluation Metric on Readmission.
Teo Kareen et al. Journal of healthcare engineering 2021 20219208138

Deep learning-based facial image analysis in medical research: a systematic review protocol.
Su Zhaohui et al. BMJ open 2021 11(11) e047549

Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study.
Kim Ji Hoon et al. BMC medical informatics and decision making 2021 21(1) 311

Quantifying the impact of addressing data challenges in prediction of length of stay.
Naemi Amin et al. BMC medical informatics and decision making 2021 21(1) 298

Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes.
Owusu Priscilla N et al. PloS one 2021 16(11) e0259499

Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders: Developing Interpretable Bayesian Machine Learning Classifiers.
Xie Wenxiu et al. Frontiers in psychiatry 2021 12771562

Beyond wellness monitoring: Continuous multiparameter remote automated monitoring of patients.
McGillion Michael H et al. The Canadian journal of cardiology 2021

Heart, Lung, Blood and Sleep Diseases

Supervised Kohonen Self-Organizing Maps of Acute Asthma from Air Pollution Exposure.
Kebalepile Moses Mogakolodi et al. International journal of environmental research and public health 2021 18(21)

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure.
Chiang Po-Han et al. IEEE journal of translational engineering in health and medicine 2021 92700513

Electrocardiogram-based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.
Khurshid Shaan et al. Circulation 2021

Development and Validation of a Predictive Model to Identify Patients With an Ascending Thoracic Aortic Aneurysm.
Mori Makoto et al. Journal of the American Heart Association 2021 e022102

Implementation and prospective clinical validation of AI-based planning and shimming techniques in cardiac MRI.
Edalati Masoud et al. Medical physics 2021

In-home mandibular repositioning during sleep using MATRx plus predicts outcome and efficacious positioning for oral appliance treatment of obstructive sleep apnea.
Mosca Erin V et al. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine 2021

Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients.
Schmitt N et al. European radiology 2021

Prospects for cardiovascular medicine using artificial intelligence.
Kodera Satoshi et al. Journal of cardiology 2021

Open Application of Statistical and Machine Learning Models to Explore the Impact of Environmental Exposures on Health and Disease: An Asthma Use Case.
Lan Bo et al. International journal of environmental research and public health 2021 18(21)

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model.

Community-level Economic Distress, Race, and Risk of Adverse Outcomes Following Heart Failure Hospitalization among Medicare Beneficiaries.
Mentias Amgad et al. Circulation 2021

Infectious Diseases

A Novel Wearable Device for Continuous Temperature Monitoring & Fever Detection.
Verma Nishant et al. IEEE journal of translational engineering in health and medicine 2021 92700407

Estimating severe fever with thrombocytopenia syndrome transmission using machine learning methods in South Korea.
Cho Giphil et al. Scientific reports 2021 11(1) 21831

Identification of Appendicitis Using Ultrasound with the Aid of Machine Learning.
Hayashi Kentaro et al. Journal of laparoendoscopic & advanced surgical techniques. Part A 2021

A Systematic Review of Clinical Prediction Rules for the Diagnosis of Influenza.
Ebell Mark H et al. Journal of the American Board of Family Medicine : JABFM 2021 34(6) 1123-1140

Can Big Data Be Used to Monitor the Mental Health Consequences of COVID-19?
Aebi Nicola Julia et al. International journal of public health 2021 66633451

Leveraging Automated Machine Learning for the Analysis of Global Public Health Data: A Case Study in Malaria.
Manduchi Elisabetta et al. International journal of public health 2021 66614296


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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