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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 06/03/2021

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

Exploring associations between children's obesogenic behaviours and local environment using big data.
Filos Dimitris et al. JMIR mHealth and uHealth 2021

Cancer

A machine-learning modified CART algorithm informs Merkel cell carcinoma prognosis.
Cheraghlou Shayan et al. The Australasian journal of dermatology 2021

Breast cancer recurrence prediction with ensemble methods and cost-sensitive learning.
Yang Pei-Tse et al. Open medicine (Warsaw, Poland) 2021 16(1) 754-768

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.
El Ayachy Radouane et al. Frontiers in oncology 2021 11603595

Magnetic resonance imaging-based artificial intelligence model in rectal cancer.
Wang Pei-Pei et al. World journal of gastroenterology 2021 27(18) 2122-2130

A Review of Applications of Machine Learning in Mammography and Future Challenges.
Batchu Sai et al. Oncology 2021 1-8

Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system.
Hsu H-H et al. Clinical radiology 2021

Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study.
Ma Jun et al. OncoTargets and therapy 2021 143267-3280

CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas.
Gitto Salvatore et al. EBioMedicine 2021 68103407

Using convolutional neural networks to discriminate between cysts and masses in Monte Carlo-simulated dual-energy mammography.
Makeev Andrey et al. Medical physics 2021

Longitudinal structural and perfusion MRI enhanced by machine learning outperforms standalone modalities and radiological expertise in high-grade glioma surveillance.
Siakallis Loizos et al. Neuroradiology 2021

Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy.
Ziebart Andreas et al. Frontiers in oncology 2021 11668273

Quality of Life as an Indicator for Care Delivery in Clinical Oncology Using FHIR.
Beutter Chantal N L et al. Studies in health technology and informatics 2021 278110-117

Chronic Disease

Predicting brain age using machine learning algorithms: A comprehensive evaluation.
Beheshti Iman et al. IEEE journal of biomedical and health informatics 2021 PP

Diagnostic Precision in the Detection of Mild Cognitive Impairment: A Comparison of Two Approaches.
Weinstein Andrea M et al. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2021

Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.
Gayathri S et al. Physical and engineering sciences in medicine 2021

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.
Ravaut Mathieu et al. JAMA network open 2021 4(5) e2111315

Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions.

Variation in end-of-life care and hospital palliative care among hospitals and local authorities: A preliminary contribution of big data.
Gusmano Michael K et al. Palliative medicine 2021 2692163211019299

Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.
Mei Jie et al. Frontiers in aging neuroscience 2021 13633752

A remote healthcare monitoring framework for diabetes prediction using machine learning.
Ramesh Jayroop et al. Healthcare technology letters 2021 8(3) 45-57

Predicting youth diabetes risk using NHANES data and machine learning.
Vangeepuram Nita et al. Scientific reports 2021 11(1) 11212

A deep learning system for detecting diabetic retinopathy across the disease spectrum.
Dai Ling et al. Nature communications 2021 12(1) 3242

Ethical, Legal and Social Issues (ELSI)

Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues.
Rogers Wendy A et al. Bioethics 2021

General Practice

Leveraging electronic health record data to inform hospital resource management : A systematic data mining approach.
Ferrão José Carlos et al. Health care management science 2021

Wearable sensors enable personalized predictions of clinical laboratory measurements.
Dunn Jessilyn et al. Nature medicine 2021

Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care.
Clark Cheryl R et al. Journal of general internal medicine 2021

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes.

Ophthalmic Disease Detection via Deep Learning With A Novel Mixture Loss Function.
Luo Xiong et al. IEEE journal of biomedical and health informatics 2021 PP

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.
Li Lan et al. Frontiers in public health 2021 9645260

The key impact of social media, electronic health records, and websites is in the area of infectious diseases and early warning systems, and in the area of personal health, that is, on mental health and smoking and drinking prevention. However, further research is required to address privacy, trust, transparency, and interoperability to leverage the potential of data held in multiple datastores and systems.

Validation of National Early Warning Score for predicting 30-day mortality after rapid response system activation in Japan.
Naito Takaki et al. Acute medicine & surgery 2021 8(1) e666

How Artificial Intelligence for Healthcare Look Like in the Future?
Denecke Kerstin et al. Studies in health technology and informatics 2021 281860-864

A Deep Learning Framework for Automated ICD-10 Coding.
Chraibi Abdelahad et al. Studies in health technology and informatics 2021 281347-351

Federated Deep Learning Architecture for Personalized Healthcare.
Chen Helen et al. Studies in health technology and informatics 2021 281193-197

Investigating the Impact of Misinformation Sources on Health Issues: Implications for Public Health.
Isaakidou Marianna et al. Studies in health technology and informatics 2021 281494-495

The Use of Social Media for Health Research Purposes: Scoping Review.
Bour Charline et al. Journal of medical Internet research 2021 23(5) e25736

Comparing Single-Page, Multipage, and Conversational Digital Forms in Health Care: Usability Study.
Iftikhar Aleeha et al. JMIR human factors 2021 8(2) e25787

Geographic Information Systems in Spatial Epidemiology: Unveiling New Horizons in Dental Public Health.
Nayak Prajna Pramod et al. Journal of International Society of Preventive & Community Dentistry 2021 11(2) 125-131

Technology assessment framework for precision health applications.
Hussain M Sazzad et al. International journal of technology assessment in health care 2021 37(1) e67

For whom should psychotherapy focus on problem coping? A machine learning algorithm for treatment personalization.
Gómez Penedo Juan Martin et al. Psychotherapy research : journal of the Society for Psychotherapy Research 2021 1-14

Technological progress in electronic health record system optimization: Systematic review of systematic literature reviews.
Negro-Calduch Elsa et al. International journal of medical informatics 2021 152104507

Development and internal validation of a predictive model of cognitive decline 36 months following elective surgery.
Jones Richard N et al. Alzheimer's & dementia (Amsterdam, Netherlands) 2021 13(1) e12201

Heart, Lung, Blood and Sleep Diseases

Guided Internet-Based Cognitive Behavioral Therapy for Insomnia: Health-Economic Evaluation From the Societal and Public Health Care Perspective Alongside a Randomized Controlled Trial.
Buntrock Claudia et al. Journal of medical Internet research 2021 23(5) e25609

Batch Enrollment for an Artificial Intelligence-Guided Intervention to Lower Neurologic Events in Patients with Undiagnosed Atrial Fibrillation (BEAGLE): Rationale and design of a digital clinical trial.
Yao Xiaoxi et al. American heart journal 2021

Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study.
Cui Song et al. Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2021 8(10) 2003893

Development and validation of the Tool for Pharmacists to Predict 30-day hospital readmission in patients with Heart Failure (ToPP-HF).
Riester Melissa R et al. American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists 2021

Electronic Phenotyping to Identify Patients with Heart Failure Using a National Clinical Information Database in Japan.
Nakayama Masaharu et al. Studies in health technology and informatics 2021 281243-247

Preliminary Qualitative Evaluation of Patient-Related Perspectives Related to the Implementation of a Predictive Algorithm in a Telehealth System for COPD.
Bender Clara et al. Studies in health technology and informatics 2021 281545-549

Artificial intelligence in clinical decision support and outcome prediction - applications in stroke.
Yeo Melissa et al. Journal of medical imaging and radiation oncology 2021

Machine learning-based risk profile classification of patients undergoing elective heart valve surgery.
Bodenhofer Ulrich et al. European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery 2021

Predictive Monitoring: IMPact in Acute Care Cardiology Trial (PM-IMPACCT) - A Randomized Clinical Trial Protocol.
Keim-Malpass Jessica et al. JMIR research protocols 2021

Infectious Diseases

Predictive variables for peripheral neuropathy in treated HIV-1 infection revealed by machine learning.
Tu Wei et al. AIDS (London, England) 2021

A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs.
Mori Mizuho et al. Odontology 2021

On the Efficiency of Machine Learning Models in Malaria Prediction.
Mbaye Ousseynou et al. Studies in health technology and informatics 2021 281437-441

How to Identify Potential Candidates for HIV Pre-Exposure Prophylaxis: An AI Algorithm Reusing Real-World Hospital Data.
Duthe Jean-Charles et al. Studies in health technology and informatics 2021 281714-718


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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