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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 11/10/2021

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

Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.
Kornblith Aaron E et al. Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine 2021

Artificial Intelligence in Rehabilitation Targeting the Participation of Children and Youth With Disabilities: Scoping Review.
Kaelin Vera C et al. Journal of medical Internet research 2021 23(11) e25745

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.
Matsushita Felipe Yu et al. European journal of pediatrics 2021

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.
Helman Stephanie M et al. Cardiology in the young 2021 1-11

Cancer

Automated operative workflow analysis of endoscopic pituitary surgery using machine learning: development and preclinical evaluation (IDEAL stage 0).
Khan Danyal Z et al. Journal of neurosurgery 2021 1-8

The altered serum lipidome and its diagnostic potential for Non-Alcoholic Fatty Liver (NAFL)-associated hepatocellular carcinoma.
Lewinska Monika et al. EBioMedicine 2021 73103661

Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans.
Masoudi Samira et al. IEEE access : practical innovations, open solutions 2021 987531-87542

Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification.
Huang Wei et al. JAMA network open 2021 4(11) e2132554

The Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development. However, its clinical application remains limited by interobserver variability in grading and quantification, which has negative consequences for risk assessment and clinical management of prostate cancer.To examine the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification.

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images.
Yu Gang et al. Nature communications 2021 12(1) 6311

Classification of histopathological images of breast cancer using an improved convolutional neural network model.
Yang Yunfeng et al. Journal of X-ray science and technology 2021

Comorbid insomnia among breast cancer survivors and its prediction using machine learning: a nationwide study in Japan.
Ueno Taro et al. Japanese journal of clinical oncology 2021

Machine Learning-Based Surveillance Strategy after Complete Ablation of Initially Recurrent Hepatocellular Carcinoma: Worth the Risk?
Nam David et al. Journal of vascular and interventional radiology : JVIR 2021 32(11) 1558-1559

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study.
Wei Liwei et al. Frontiers in oncology 2021 11763381

Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures.
Lin Ching-Kai et al. Cancer medicine 2021

Chronic Disease

Deep learning models for screening of high myopia using optical coherence tomography.
Choi Kyung Jun et al. Scientific reports 2021 11(1) 21663

AI MSK clinical applications: cartilage and osteoarthritis.
Joseph Gabby B et al. Skeletal radiology 2021

Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review.
Tan Kuo Ren et al. Journal of diabetes science and technology 2021 19322968211056917

Initial Clinical Experience With a Simple, Home System for Early Detection and Monitoring of Diabetic Foot Ulcers: The Foot Selfie.
Swerdlow Mark et al. Journal of diabetes science and technology 2021 19322968211053348

ARTIFICIAL INTELLIGENCE IN DIABETIC RETINOPATHY SCREENING. A REVIEW.
Stranák Z et al. Ceska a slovenska oftalmologie : casopis Ceske oftalmologicke spolecnosti a Slovenske oftalmologicke spolecnosti 2021 1(Ahead of print) 1-8

Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study.
O'Brien Megan K et al. Digital biomarkers 2021 5(2) 167-175

Ethical, Legal and Social Issues (ELSI)

AI in Cardiovascular Imaging: "Unexplainable" Legal and Ethical Challenges?
Lang Michael et al. The Canadian journal of cardiology 2021

Trust in AI: why we should be designing for APPROPRIATE reliance.
Benda Natalie C et al. Journal of the American Medical Informatics Association : JAMIA 2021

General Practice

Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development.
Bradshaw Tyler J et al. Journal of nuclear medicine : official publication, Society of Nuclear Medicine 2021

Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases.
Visaggi Pierfrancesco et al. Journal of clinical gastroenterology 2021

Big Data and Real-World Data based Cost-Effectiveness Studies and Decision-making Models: A Systematic Review and Analysis.
Lu Z Kevin et al. Frontiers in pharmacology 2021 12700012

A Machine Learning Approach to Predicting New-onset Depression in a Military Population.
Sampson Laura et al. Psychiatric research and clinical practice 2021 3(3) 115-122

Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning.
Kim Kyung-Won et al. Psychiatry investigation 2021

Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach.
Weller Orion et al. PloS one 2021 16(11) e0258535

Addressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While previous research has identified individual risk and protective factors associated with many adolescent social morbidities, modern machine learning approaches can help identify risk and protective factors that interact (group) to provide predictive power for STB. This study aims to develop a prediction algorithm for STB among adolescents using the risk and protective factor framework and social determinants of health.The sample population consisted of more than 179,000 high school students living in Utah and participating in the Communities That Care (CTC) Youth Survey from 2011-2017.

Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study.
Lu Zhaohua et al. Journal of medical Internet research 2021 23(11) e26777

External Validation of a Machine Learning Classifier to Identify Unhealthy Alcohol Use in Hospitalized Patients.
Lin Yiqi et al. Addiction (Abingdon, England) 2021

Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.
Chmiel F P et al. Scientific reports 2021 11(1) 21513

Machine learning techniques for mortality prediction in emergency departments: a systematic review.
Naemi Amin et al. BMJ open 2021 11(11) e052663

Developing an Analytical Pipeline to Classify Patient Safety Event Reports Using Optimized Predictive Algorithms.
Adadey Asa et al. Methods of information in medicine 2021

Prediction of Bedridden Duration of Hospitalized Patients by Machine Learning Based on EMRs at Admission.
Lin Weijie et al. Computers, informatics, nursing : CIN 2021

Lower AM-PAC "6-Clicks" Basic Mobility Score Predicts Discharge to a Postacute Care Facility among Patients in Cardiac Intensive Care Units.
Whitlock Katelyn C et al. Physical therapy 2021

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.
Qureshi Zeeshan et al. Computational and mathematical methods in medicine 2021 20212376391

Surveillance in Next-Generation Personalized Healthcare: Science and Ethics of Data Analytics in Healthcare.
Althobaiti Kamal et al. The New bioethics : a multidisciplinary journal of biotechnology and the body 2021 1-25

Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach.
Rodrigo Hansapani et al. Journal of medical Internet research 2021 23(11) e28999

Blockchain Integration With Digital Technology and the Future of Health Care Ecosystems: Systematic Review.
Fatoum Hanaa et al. Journal of medical Internet research 2021 23(11) e19846

Heart, Lung, Blood and Sleep Diseases

Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.
Grant Lars et al. Internal and emergency medicine 2021

Clinical Phenotyping of Out-of-Hospital Cardiac Arrest Patients With Shockable Rhythm　- Machine Learning-Based Unsupervised Cluster Analysis.
Okada Yohei et al. Circulation journal : official journal of the Japanese Circulation Society 2021

Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence.
Nadarajah Ramesh et al. BMJ open 2021 11(11) e052887

Diagnostic Performance of Machine Learning-Derived Obstructive Sleep Apnea Prediction Tools in Large Clinical and Community-based Samples.
Holfinger Steven J et al. Chest 2021

Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction.
Wang Suhuai et al. BMC medical informatics and decision making 2021 21(1) 301

Infectious Diseases

Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media.
Tao Dandan et al. Scientific reports 2021 11(1) 21678

In this work, the dual-task BERTweet model was developed to identify unreported foodborne illnesses and extract foodborne-illness-related entities from Twitter. Unlike previous methods, our model leveraged the mutually beneficial relationships between the two tasks. The results showed that the F1-score of relevance prediction was 0.87, and the F1-score of entity extraction was 0.61. Key elements such as time, location, and food detected from sentences indicating foodborne illnesses were used to analyze potential foodborne outbreaks in massive historical tweets. A case study on tweets indicating foodborne illnesses showed that the discovered trend is consistent with the true outbreaks that occurred during the same period.

Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study.
Garcia-Zamalloa Alberto et al. PloS one 2021 16(11) e0259203

Retrospective analysis and time series forecasting with automated machine learning of ascariasis, enterobiasis and cystic echinococcosis in Romania.
Benecke Johannes et al. PLoS neglected tropical diseases 2021 15(11) e0009831

Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis.
Li Dingchen et al. Frontiers in cellular and infection microbiology 2021 11742062

Reproductive Health

Novelelectronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms.
Li Yi-Xin et al. Pregnancy hypertension 2021 26102-109


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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