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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 10/07/2021

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

Provider Perspectives on the Acceptability, Appropriateness, and Feasibility of Teleneonatology.
Fang Jennifer L et al. American journal of perinatology 2021

A new approach to a legacy concern: Evaluating machine-learned Bayesian networks to predict childhood lead exposure risk from community water systems.
Mulhern Riley et al. Environmental research 2021 204(Pt B) 112146

Cancer

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.
Bedrikovetski Sergei et al. BMC cancer 2021 21(1) 1058

Magnetic Resonance Radiomics and Machine-learning Models: An Approach for Evaluating Tumor-stroma Ratio in Patients with Pancreatic Ductal Adenocarcinoma.
Meng Yinghao et al. Academic radiology 2021

Multi-Center Follow-up Study to Develop a Classification System Which Differentiates Mucinous Cystic Neoplasm of the Liver and Benign Hepatic Cyst Using Machine Learning.
Hardie Andrew D et al. Academic radiology 2021

Improved predictive performance of prostate biopsy collaborative group risk calculator when based on automated machine learning.
Stojadinovic Miroslav et al. Computers in biology and medicine 2021 138104903

Radiomics in Oncology: A Practical Guide.
Shur Joshua D et al. Radiographics : a review publication of the Radiological Society of North America, Inc 2021 41(6) 1717-1732

Construction and validation of nomograms for non-metastatic Ewing sarcoma: A prognostic factor analysis based on the SEER database.
Huang Runzhi et al. Oncology letters 2021 22(5) 777

Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning.
Yang Charles Q et al. PloS one 2021 16(9) e0257949

A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy.
Dov David et al. Archives of pathology & laboratory medicine 2021

The application of radiomics in laryngeal cancer.
Rajgor Amarkumar Dhirajlal et al. The British journal of radiology 2021 20210499

Chronic Disease

A Systematic Review of Machine Learning Based Gait characteristics in Parkinson's disease.
Sharma Pooja et al. Mini reviews in medicinal chemistry 2021

The Importance of Close Follow-Up in Patients with Early-Grade Diabetic Retinopathy: A Taiwan Population-Based Study Grading via Deep Learning Model.
Lee Chia-Cheng et al. International journal of environmental research and public health 2021 18(18)

Use of predictive models to identify patients who are likely to benefit from refraction at a follow-up visit after cataract surgery.
Gupta Sachin et al. Indian journal of ophthalmology 2021 69(10) 2695-2701

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.
Hara Konan et al. PloS one 2021 16(9) e0254394

Estimation of Baseline Serum Creatinine with Machine Learning.
Ghosh Erina et al. American journal of nephrology 2021 1-10

Using Machine Learning to Capture Quality Metrics from Natural Language: A Case Study of Diabetic Eye Exams.
Fong Allan et al. Methods of information in medicine 2021

Data-Driven Prediction of Fatigue in Parkinson's Disease Patients.
Lee Dong Goo et al. Frontiers in artificial intelligence 2021 4678678

Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease.
Ricci Laetitia et al. Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver 2021

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features.
Bede Peter et al. Journal of neurology 2021

A UK-Wide Study Employing Natural Language Processing to Determine What Matters to People about Brain Health to Improve Drug Development: The Electronic Person-Specific Outcome Measure (ePSOM) Programme.
Saunders S et al. The journal of prevention of Alzheimer's disease 2021 8(4) 448-456

Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.
Grueso Sergio et al. Alzheimer's research & therapy 2021 13(1) 162

In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance.Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals

Ethical, Legal and Social Issues (ELSI)

Ethics of artificial intelligence in global health: Explainability, algorithmic bias and trust.
Kerasidou Angeliki et al. Journal of oral biology and craniofacial research 2021 11(4) 612-614

General Practice

Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients.
Na Pattalung Thanakron et al. Journal of personalized medicine 2021 11(9)

Heterogeneous Effects of Health Insurance on Rural Children's Health in China: A Causal Machine Learning Approach.
Chen Hua et al. International journal of environmental research and public health 2021 18(18)

Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases.
Pecere Silvia et al. Diagnostics (Basel, Switzerland) 2021 11(9)

Quantifying representativeness in randomized clinical trials using machine learning fairness metrics.
Qi Miao et al. JAMIA open 2021 4(3) ooab077

Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future.
Hameed B M Zeeshan et al. Therapeutic advances in urology 2021 1317562872211044880

Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning.
Grassi Massimiliano et al. Journal of affective disorders 2021 296117-125

Evaluation and Real-World Performance Monitoring of Artificial Intelligence Models in Clinical Practice Purchase: Try It, Buy It, Check It.
Allen Bibb et al. Journal of the American College of Radiology : JACR 2021

In this article, we discuss why regulatory clearance alone may not be enough to ensure AI will be safe and effective in all radiological practices and review strategies available resources for evaluating before clinical use and monitoring performance of AI models to ensure efficacy and patient safety

Integrating digital pathology into clinical practice running title: clinical implementation of digital pathology.
Hanna Matthew G et al. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2021

Binge drinking in early adulthood: A machine learning approach.
Dell Nathaniel A et al. Addictive behaviors 2021 124107122

Perspectives: A surgeon's guide to machine learning.
Kuo Rachel Yl et al. International journal of surgery (London, England) 2021 106133

[Improved Mental Health Clinical Practice Informed by Digital Phenotyping].
Bougeard Alan et al. Sante mentale au Quebec 2021 46(1) 135-136

Medical Data Mining Course Development in Postgraduate Medical Education: Web-Based Survey and Case Study.
Yang Lin et al. JMIR medical education 2021 7(4) e24027

VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models.
Cheng Furui et al. IEEE transactions on visualization and computer graphics 2021 PP

Construction and Implementation of Big Data in Healthcare in Yichang City, Hubei Province.
Lu Fangfang et al. China CDC weekly 2021 3(1) 14-17

Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations.
Mouchabac Stephane et al. Journal of medical Internet research 2021 23(9) e24560

Evaluation of the Diet Tracking Smartphone Application Keenoa: A Qualitative Analysis.
Bouzo Valerie et al. Canadian journal of dietetic practice and research : a publication of Dietitians of Canada = Revue canadienne de la pratique et de la recherche en dietetique : une publication des Dietetistes du Canada 2021 1-5

Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts.
Sarker Abeed et al. JMIR medical informatics 2021 9(9) e18471

Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial.
Eng David K et al. Radiology 2021 204021

Heart, Lung, Blood and Sleep Diseases

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.
Schrumpf Fabian et al. Sensors (Basel, Switzerland) 2021 21(18)

The TVGH-NYCU Thal-Classifier: Development of a Machine-Learning Classifier for Differentiating Thalassemia and Non-Thalassemia Patients.
Fu Yi-Kai et al. Diagnostics (Basel, Switzerland) 2021 11(9)

A Personalized Medical Decision Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data from the Real World.
Gu Dongxiao et al. Diagnostics (Basel, Switzerland) 2021 11(9)

Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment.
van Leeuwen Kicky G et al. Insights into imaging 2021 12(1) 133

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.
Lee Reeree et al. European journal of nuclear medicine and molecular imaging 2021

Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study.
Zelnick Leila R et al. Clinical journal of the American Society of Nephrology : CJASN 2021 16(7) 1015-1024

Prediction of response after cardiac resynchronization therapy with machine learning.
Liang Yixiu et al. International journal of cardiology 2021

Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure.
Wang Qi et al. ESC heart failure 2021

Infectious Diseases

Early Evaluation of an Ultra-Portable X-ray System for Tuberculosis Active Case Finding.
Vo Luan Nguyen Quang et al. Tropical medicine and infectious disease 2021 6(3)

A Machine Learning Sepsis Prediction Algorithm for Intended Intensive Care Unit Use (NAVOY Sepsis): Proof-of-Concept Study.
Persson Inger et al. JMIR formative research 2021 5(9) e28000

Despite decades of research, sepsis remains a leading cause of mortality and morbidity in intensive care units worldwide. The key to effective management and patient outcome is early detection, for which no prospectively validated machine learning prediction algorithm is currently available for clinical use in Europe.We aimed to develop a high-performance machine learning sepsis prediction algorithm based on routinely collected intensive care unit data.

Deep Learning for Discrimination Between Fungal Keratitis and Bacterial Keratitis: DeepKeratitis.
Ghosh Amit Kumar et al. Cornea 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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