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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 03/11/2021

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

Cancer

A prognostic system for epithelial ovarian carcinomas using machine learning.
Grimley Philip M et al. Acta obstetricia et gynecologica Scandinavica 2021

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value.
Chamberlin Jordan et al. BMC medicine 2021 19(1) 55

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.
Jones Owain T et al. Journal of medical Internet research 2021 23(3) e23483

This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated.

Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma: A multi-site validation study.
Koyuncu Can F et al. The Journal of clinical investigation 2021

Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease.
Lupsor-Platon Monica et al. Cancers 2021 13(4)

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.
Tataru Octavian Sabin et al. Diagnostics (Basel, Switzerland) 2021 11(2)

A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade.
Castillo T Jose M et al. Diagnostics (Basel, Switzerland) 2021 11(2)

Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model.
Gentile Francesco et al. Diagnostics (Basel, Switzerland) 2021 11(2)

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.
Ugga Lorenzo et al. Neuroradiology 2021

Chronic Disease

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study.
Lim Hyun Kyung et al. PloS one 2021 16(3) e0247330

Identifying Key Determinants of Childhood Obesity: A Narrative Review of Machine Learning Studies.
LeCroy Madison N et al. Childhood obesity (Print) 2021

Assessment of a smartphone-based application for diabetic foot ulcer measurement.
Kuang Beatrice et al. Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society 2021

Machine learning methods to predict amyloid positivity using domain scores from cognitive tests.
Shan Guogen et al. Scientific reports 2021 11(1) 4822

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.
Li Jun et al. International journal of medical informatics 2021 149104429

General Practice

New international reporting guidelines for clinical trials evaluating effectiveness of artificial intelligence interventions in dermatology: strengthening the SPIRIT of robust trial reporting.
Charalambides M et al. The British journal of dermatology 2021 184(3) 381-383

A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology.
Scheetz Jane et al. Scientific reports 2021 11(1) 5193

AI applications to medical images: From machine learning to deep learning.
Castiglioni Isabella 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 839-24

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.
Figueroa Caroline A et al. Journal of the American Medical Informatics Association : JAMIA 2021

Using machine learning to predict suicide in the 30 days after discharge from psychiatric hospital in Denmark.
Jiang Tammy et al. The British journal of psychiatry : the journal of mental science 2021 1-8

Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age.
Wasmann Jan-Willem A et al. Ear and hearing 2021

Application of a machine learning approach to characterization of liver function using
Nakajo Masatoyo et al. Abdominal radiology (New York) 2021

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity.
Majnaric Ljiljana Trtica et al. Journal of clinical medicine 2021 10(4)

In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity.

A Review on Human-AI Interaction in Machine Learning and Insights for Medical Applications.
Maadi Mansoureh et al. International journal of environmental research and public health 2021 18(4)

Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression.
van Eeden Wessel A et al. Psychiatry research 2021 299113823

Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.
Jauk Stefanie et al. Journal of medical systems 2021 45(4) 48

Computer Vision in the Operating Room: Opportunities and Caveats.
Kennedy-Metz Lauren R et al. IEEE transactions on medical robotics and bionics 2021 3(1) 2-10

The Role of DICOM in Artificial Intelligence for Skin Disease.
Caffery Liam J et al. Frontiers in medicine 2021 7619787

Heart, Lung, Blood and Sleep Diseases

Estimation of low-density lipoprotein cholesterol by machine learning methods.
Christina Tsigalou et al. Clinica chimica acta; international journal of clinical chemistry 2021

Development of digital measures for nighttime scratch and sleep using wrist-worn wearable devices.
Mahadevan Nikhil et al. NPJ digital medicine 2021 4(1) 42

Infectious Diseases

Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach.
Shah Adnan Muhammad et al. International journal of medical informatics 2021 149104434

Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.
Ridgway Jessica P et al. Current HIV/AIDS reports 2021

Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning.
Zheng Shuang et al. Frontiers in psychology 2021 12594031

COVID-19 Impact as an Illustration of Big Data Monitoring of Clinical Practice.
Campbell Bruce C V et al. Stroke 2021 STROKEAHA120033628

Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.
Yousef Malik et al. Journal of integrative bioinformatics 2021

Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.
Mendels David-A et al. Proceedings of the National Academy of Sciences of the United States of America 2021 118(12)

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.
Alam Nur-A- et al. Sensors (Basel, Switzerland) 2021 21(4)

Factors Affecting the Survival of Early COVID-19 Patients in South Korea: An Observational Study based on the Korean National Health Insurance Big Data.
Byeon Kyeong Hyang et al. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 2021

Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.
Giacobbe Daniele Roberto et al. Frontiers in medicine 2021 8617486

We provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice.

Reproductive Health

Three machine learning algorithms and their utility in exploring risk factors associated with primary cesarean section in low-risk women: A methods paper.
Clark Rebecca R S et al. Research in nursing & health 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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