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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 05/27/2021

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 Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis.
Marcinkevics Ricards et al. Frontiers in pediatrics 2021 9662183

Is there a diagnosis-specific influence of childhood trauma on later educational attainment? A machine learning analysis in a large help-seeking sample.
Haidl Theresa Katharina et al. Journal of psychiatric research 2021 138591-597

An eHealth Framework for Managing Pediatric Growth Disorders and Growth Hormone Therapy.
Dimitri Paul et al. Journal of medical Internet research 2021 23(5) e27446

Cancer

Predicting symptomatic mesenteric mass in neuroendocrine tumors using radiomics.
Blazevic Anela et al. Endocrine-related cancer 2021

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review.
Alabi Rasheed Omobolaji et al. Artificial intelligence in medicine 2021 115102060

Augmenting lung cancer diagnosis on chest radiographs: positioning artificial intelligence to improve radiologist performance.
Tam M D B S et al. Clinical radiology 2021

Development and head-to-head comparison of machine-learning models to identify patients requiring prostate biopsy.
Yu Shuanbao et al. BMC urology 2021 21(1) 80

Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future.
Iqbal Muhammad Javed et al. Cancer cell international 2021 21(1) 270

Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry.
Kim Ji-Yeon et al. Frontiers in oncology 2021 11596364

Chronic Disease

Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning
A Boutet et al, Nat Comms, ay 24, 2021

Automated Quantification of Pathological Fluids in Neovascular Age-Related Macular Degeneration, and Its Repeatability Using Deep Learning.
Mantel Irmela et al. Translational vision science & technology 2021 10(4) 17

Association rule learning in neuropsychological data analysis for Alzheimer's disease.
Happawana Keith A et al. Journal of neuropsychology 2021

Explainable AI enables clinical trial patient selection to retrospectively improve treatment effects in schizophrenia.
Mellem Monika S et al. BMC medical informatics and decision making 2021 21(1) 162

Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures
S Palmqvist et al, Nature Medicine, May 24, 2021

A combination of plasma phospho-tau (P-tau) and other accessible biomarkers might provide accurate prediction about the risk of developing Alzheimer’s disease (AD) dementia. We examined this in participants with subjective cognitive decline and mild cognitive impairment from the BioFINDER (n?=?340) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n?=?543) studies. Plasma P-tau, plasma Aß42/Aß40, plasma neurofilament light, APOE genotype, brief cognitive tests and an AD-specific magnetic resonance imaging measure were examined using progression to AD as outcome. Within 4 years, plasma P-tau217 predicted AD accurately (area under the curve (AUC) = 0.83) in BioFINDER. Combining plasma P-tau217, memory, executive function and APOE produced higher accuracy (AUC = 0.91, P?<?0.001).

Feasibility of a generalized convolutional neural network for automated identification of vertebral compression fractures: The Manitoba Bone Mineral Density Registry.
Monchka Barret A et al. Bone 2021 150116017

Application of Artificial Intelligence Modeling Technology Based on Multi-Omics in Noninvasive Diagnosis of Inflammatory Bowel Disease.
Huang Qiongrong et al. Journal of inflammation research 2021 141933-1943

Validation of a machine learning approach to estimate Systemic Lupus Erythematosus Disease Activity Index score categories and application in a real-world dataset.
Alves Pedro et al. RMD open 2021 7(2)

Social Network Analysis of an Online Smoking Cessation Community to Identify Users' Smoking Status.
Shah Adnan Muhammad et al. Healthcare informatics research 2021 27(2) 116-126

Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.
Gubatan John et al. World journal of gastroenterology 2021 27(17) 1920-1935

Ethical, Legal and Social Issues (ELSI)

Artificial Intelligence to Reduce Ocular Health Disparities: Moving From Concept to Implementation.
Campbell John P et al. Translational vision science & technology 2021 10(3) 19

How to be responsible in AI publication
Nature Machine Intelligence, editorial, May 19,2021

A white paper from Partnership on AI provides timely advice on tackling the urgent challenge of navigating risks of AI research and responsible publication. AI research has quickly developed in the past two decades from a niche topic to one that has transformed whole areas of technology, society and scientific research. Remarkably, much of the work has taken place without much ethical oversight.

Ethical evaluation of artificial intelligence applications in radiotherapy using the Four Topics Approach.
Yirmibesoglu Erkal Eda et al. Artificial intelligence in medicine 2021 115102055

General Practice

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative.
Ronquillo Charlene Esteban et al. Journal of advanced nursing 2021

Construction and Evaluation of a Deep Learning Model for Assessing Acne Vulgaris Using Clinical Images.
Yang Yin et al. Dermatology and therapy 2021

State-of-the-art reviews predictive modeling in adult spinal deformity: applications of advanced analytics.
Joshi Rushikesh S et al. Spine deformity 2021

The promise of machine learning in predicting treatment outcomes in psychiatry.
Chekroud Adam M et al. World psychiatry : official journal of the World Psychiatric Association (WPA) 2021 20(2) 154-170

In-house development, implementation and evaluation of machine learning software for automated clinical scan processing.
Taylor Jonathan C et al. Nuclear medicine communications 2021

Survival in the Intensive Care Unit: A prognosis model based on Bayesian classifiers.
Delgado Rosario et al. Artificial intelligence in medicine 2021 115102054

Detecting pathological features and predicting fracture risk from dual-energy X-ray absorptiometry images using deep learning.
Nissinen Tomi et al. Bone reports 2021 14101070

Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design.
Izumi Keisuke et al. Frontiers in psychiatry 2021 12611243

Wearable sensors enable personalized predictions of clinical laboratory measurements
J Dunn et al, Nature Medicine, May 24, 2021

We examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic

Evaluating diagnostic content of AI-generated radiology reports of chest X-rays.
Babar Zaheer et al. Artificial intelligence in medicine 2021 116102075

Generalisability through local validation: overcoming barriers due to data disparity in healthcare.
Mitchell William Greig et al. BMC ophthalmology 2021 21(1) 228

Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data.
Sato Noriaki et al. Computer methods and programs in biomedicine 2021 206106129

Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope?
De Hond Anne et al. International journal of medical informatics 2021 152104496

Application and Construction of Deep Learning Networks in Medical Imaging.
Torres-Velázquez Maribel et al. IEEE transactions on radiation and plasma medical sciences 2021 5(2) 137-159

Artificial intelligence for thoracic radiology: from research tool to clinical practice.
Calandriello Lucio et al. The European respiratory journal 2021 57(5)

Review of Data Pulse: A Brief Tour of Artificial Intelligence in Health Care.
Manson Lesley et al. Families, systems & health : the journal of collaborative family healthcare 2021 39(1) 153-154

Emergency department frequent user subgroups: Development of an empirical, theory-grounded definition using population health data and machine learning.
Goodman Jessica M et al. Families, systems & health : the journal of collaborative family healthcare 2021 39(1) 55-65

Rethinking PICO in the Machine Learning Era: ML-PICO.
Liu Xinran et al. Applied clinical informatics 2021 12(2) 407-416

ARTIFICIAL INTELLIGENCE IN MEDICAL DEVICES: PAST, PRESENT AND FUTURE.
Badnjevic Almir et al. Psychiatria Danubina 2021 33(Suppl 3) 101-106

Heart, Lung, Blood and Sleep Diseases

Assessing Ischemic Stroke with Convolutional Image Features in Carotid Color Doppler.
Lo Chung-Ming et al. Ultrasound in medicine & biology 2021

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study.
Sekelj Sara et al. European journal of preventive cardiology 2021 28(6) 598-605

Comparative Effectiveness of Machine Learning Approaches for Predicting Gastrointestinal Bleeds in Patients Receiving Antithrombotic Treatment.
Herrin Jeph et al. JAMA network open 2021 4(5) e2110703

Machine learning approaches to surrogate multifidelity Growth and Remodeling models for efficient abdominal aortic aneurysmal applications.
Jiang Zhenxiang et al. Computers in biology and medicine 2021 133104394

Effects of air pollution in Spatio-temporal modeling of asthma-prone areas using a machine learning model.
Razavi-Termeh Seyed Vahid et al. Environmental research 2021 111344

Intelligent Cardiovascular Disease Prediction Empowered with Gradient Descent Optimization.
Nawaz Muhammad Saqib et al. Heliyon 2021 7(5) e06948

Infectious Diseases

Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning.
Grais Emad M et al. Scientific reports 2021 11(1) 10643

A comparison of covariate selection techniques applied to pre-exposure prophylaxis (PrEP) drug concentration data in men and transgender women at risk for HIV.
Peterson Skyler et al. Journal of pharmacokinetics and pharmacodynamics 2021

Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.
Poirier Canelle et al. PloS one 2021 16(5) e0250890


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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