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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 11/04/2021

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

Simplified Pediatric Index of Mortality 3 Score by Explainable Machine Learning Algorithm.
Baloglu Orkun et al. Critical care explorations 2021 3(10) e0561

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones.
Fenta Haile Mekonnen et al. BMC medical informatics and decision making 2021 21(1) 291

Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.
Yang Jing et al. BMC psychiatry 2021 21(1) 535

Cancer

Machine learning-based rapid diagnosis of human borderline ovarian cancer on second-harmonic generation images.
Wang Guangxing et al. Biomedical optics express 2021 12(9) 5658-5669

Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20 B cells in patients with pancreatic ductal adenocarcinoma.
Yu Jieyu et al. Abdominal radiology (New York) 2021

Risk Stratifying Indeterminate Thyroid Nodules With Machine Learning.
Luong George et al. The Journal of surgical research 2021 270214-220

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.
Kourou Konstantina et al. Computational and structural biotechnology journal 2021 195546-5555

Deep Learning for Lung Cancer Nodal Staging and Real-World Clinical Practice.
Park Chang Min et al. Radiology 2021 211981

Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer.
Zhong Yifan et al. Radiology 2021 210902

A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma.
Shaban Muhammad et al. The Journal of pathology 2021

Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach.
Dong Weichuan et al. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 2021

MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region.
Malik Nauman et al. Journal of neuro-oncology 2021

Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.
Satake Hiroko et al. La Radiologia medica 2021

Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers.
Devakumar Devadhas et al. Journal of medical physics 2021 46(3) 181-188

Machine Learning Classifier to Reproduce Lung Metastases Tumor Board Decisions.
Seo C et al. International journal of radiation oncology, biology, physics 2021 111(3S) e117-e118

Development and Validation of Deep Learning Model Based on CT Simulator Images to Predict 5-Year Recurrence of Stage IIIA-N2 Non-Small-Cell Lung Cancer Patients Treated with Surgery and Postoperative Radiotherapy.
Ma Z et al. International journal of radiation oncology, biology, physics 2021 111(3S) e114

Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images.
Hamada Kenta et al. Journal of gastroenterology and hepatology 2021

Chronic Disease

Predicting future amyloid biomarkers in dementia patients with machine learning to improve clinical trial patient selection.
Reith Fabian H et al. Alzheimer's & dementia (New York, N. Y.) 2021 7(1) e12212

Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India.
Birk Nick et al. The Journal of nutrition 2021 151(Supplement_2) 110S-118S

Machine Learning-Based Prediction for 4-Year Risk of Metabolic Syndrome in Adults: A Retrospective Cohort Study.
Zhang Hui et al. Risk management and healthcare policy 2021 144361-4368

Using Machine Learning and the National Health and Nutrition Examination Survey to Classify Individuals With Hearing Loss.
Ellis Gregory M et al. Frontiers in digital health 2021 3723533

Predictive performance and impact of algorithms in remote monitoring of chronic conditions: A systematic review and meta-analysis.
Castelyn Grant et al. International journal of medical informatics 2021 156104620

Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study.
Maeda Yasuharu et al. Gastrointestinal endoscopy 2021

A nomogram-based diabetic sensorimotor polyneuropathy severity prediction using Michigan neuropathy screening instrumentations.
Haque Fahmida et al. Computers in biology and medicine 2021 139104954

Ethical, Legal and Social Issues (ELSI)

In support of "no-fault" civil liability rules for artificial intelligence.
Marchisio Emiliano et al. SN social sciences 2021 1(2) 54

Regulatory Issues and Challenges to Artificial Intelligence Adoption.
Harvey Harlan Benjamin et al. Radiologic clinics of North America 2021 59(6) 1075-1083

General Practice

The Emergence and Future of Public Health Data Science.
Goldsmith Jeff et al. Public health reviews 2021 421604023

Data science is a newly-formed and, as yet, loosely-defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term "data science," particularly as it relates to public health; to identify ways that data science methods can strengthen public health research; to propose ways to strengthen education for public health data science; and to discuss issues in data science that may benefit from a public health perspective

A Roadmap for Building Data Science Capacity for Health Discovery and Innovation in Africa.
Beyene Joseph et al. Frontiers in public health 2021 9710961

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.
Bernstam Elmer V et al. Clinical and translational science 2021

Discovering Composite Lifestyle Biomarkers With Artificial Intelligence From Clinical Studies to Enable Smart eHealth and Digital Therapeutic Services.
Kyriazakos Sofoklis et al. Frontiers in digital health 2021 3648190

Accessing Artificial Intelligence for Clinical Decision-Making.
Giordano Chris et al. Frontiers in digital health 2021 3645232

Success Factors of Artificial Intelligence Implementation in Healthcare.
Wolff Justus et al. Frontiers in digital health 2021 3594971

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes.
Ilan Yaron et al. Frontiers in digital health 2021 2569178

Clinical impact and quality of randomized controlled trials involving interventions evaluating artificial intelligence prediction tools: a systematic review.
Zhou Qian et al. NPJ digital medicine 2021 4(1) 154

The false hope of current approaches to explainable artificial intelligence in health care.
Ghassemi Marzyeh et al. The Lancet. Digital health 2021 3(11) e745-e750

The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of bias

Prediction of recurrent suicidal behavior among suicide attempters with Cox regression and machine learning: a 10-year prospective cohort study.
Wei Yan-Xin et al. Journal of psychiatric research 2021 144217-224

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling.
Rykov Yuri et al. JMIR mHealth and uHealth 2021 9(10) e24872

Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption.
Landers Matthew et al. Digital biomarkers 2021 5(3) 216-223

Personalized multicomponent exercise programs using smartphone technology among older people: protocol for a randomized controlled trial.
Netz Yael et al. BMC geriatrics 2021 21(1) 605

Assessing the Robustness and Performance of Artificial Intelligence Powered Planning Tools in Clinical Settings.
Hito M et al. International journal of radiation oncology, biology, physics 2021 111(3S) e91

Google street view image availability in the Bronx and San Diego, 2007-2020: Understanding potential biases in virtual audits of urban built environments.
Smith Cara M et al. Health & place 2021 72102701

Public Attitudes to Digital Health Research Repositories: Cross-sectional International Survey.
Nunes Vilaza Giovanna et al. Journal of medical Internet research 2021 23(10) e31294

Heterogeneous treatment effect analysis based on machine-learning methodology.
Gong Xiajing et al. CPT: pharmacometrics & systems pharmacology 2021

Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.
Henn Jonas et al. Langenbeck's archives of surgery 2021

Heart, Lung, Blood and Sleep Diseases

Deep Learning Model for Automatic Contouring of Cardiovascular Substructures on Radiotherapy Planning CT Images: Dosimetric Validation and Reader Study based Clinical Acceptability Testing.
Garrett Fernandes Miguel et al. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2021

Accuracy and Prognostic Role of NCCT-ASPECTS Depend on Time from Acute Stroke Symptom-onset for both Human and Machine-learning Based Evaluation.
Potreck A et al. Clinical neuroradiology 2021

Identifying Heart Failure in ECG Data With Artificial Intelligence-A Meta-Analysis.
Grün Dimitri et al. Frontiers in digital health 2021 2584555

Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study.
Golbus Jessica R et al. The Lancet. Digital health 2021 3(11) e707-e715

Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings.
Zhang Peng et al. Computers in biology and medicine 2021 139104880

Prediction of large vessel occlusion for ischaemic stroke by using the machine learning model random forests.
Wang Jianan et al. Stroke and vascular neurology 2021

What will we ask to artificial intelligence for cardiovascular medicine in the next decade?
Gallone Guglielmo et al. Minerva cardiology and angiology 2021

Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss.
Nikolaou Vasilis et al. BMJ open respiratory research 2021 8(1)

Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine.
Sharma Abhinav et al. Diabetes care 2021

Reproductive Health

Can ensemble machine learning improve the accuracy of severe maternal morbidity screening in a perinatal database?
Cartus Abigail R et al. Epidemiology (Cambridge, Mass.) 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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