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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 09/23/2021

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

Perinatal health predictors using artificial intelligence: A review.
Ramakrishnan Rema et al. Women's health (London, England) 2021 1717455065211046132

Assess A Smartphone App (AICaries) that uses artificial intelligence to detect dental caries in children and provide interactive oral health education: Protocol for design and usability testing.
Xiao Jin et al. JMIR research protocols 2021

Combining Resampling Strategies and Ensemble Machine Learning Methods to Enhance Prediction of Neonates with a Low Apgar Score After Induction of Labor in Northern Tanzania.
Tarimo Clifford Silver et al. Risk management and healthcare policy 2021 143711-3720

Cancer

Deep learning classification of cervical dysplasia using depth-resolved angular light scattering profiles.
Zhang Haoran et al. Biomedical optics express 2021 12(8) 4997-5007

Deep learning applications in neuro-oncology.
Khan Adnan A et al. Surgical neurology international 2021 12435

A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks.
Altaf Meteb M et al. Mathematical biosciences and engineering : MBE 2021 18(5) 5029-5046

Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.
Jena Biswajit et al. Computers in biology and medicine 2021 137104803

Artificial intelligence in cancer research, diagnosis and therapy.
Elemento Olivier et al. Nature reviews. Cancer 2021

In this Viewpoint article, we asked four experts for their opinions on how we can begin to implement artificial intelligence while ensuring standards are maintained so as transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery.

Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study.
Starmans Martijn P A et al. Clinical & experimental metastasis 2021

Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: a multicenter study.
Yuan Xiang-Lei et al. Journal of gastroenterology and hepatology 2021

Artificial intelligence or colonoscopy quality the likes of which have never been seen.
Marlicz Wojciech et al. Gastrointestinal endoscopy 2021 94(4) 872-873

A systematic review and meta-analysis of diagnostic performance and physicians' perceptions of artificial intelligence (AI)-assisted CT diagnostic technology for the classification of pulmonary nodules.
Huang Guo et al. Journal of thoracic disease 2021 13(8) 4797-4811

Predicting the risk of cancer in adults using supervised machine learning: a scoping review.
Abdullah Alfayez Asma et al. BMJ open 2021 11(9) e047755

Deep Learning-Based Prediction of Future Extrahepatic Metastasis and Macrovascular Invasion in Hepatocellular Carcinoma.
Fu Sirui et al. Journal of hepatocellular carcinoma 2021 81065-1076

Chronic Disease

Heuristic scoring method utilizing FDG-PET statistical parametric mapping in the evaluation of suspected Alzheimer disease and frontotemporal lobar degeneration.
Ford Jeremy N et al. American journal of nuclear medicine and molecular imaging 2021 11(4) 313-326

Positive impact of short-term gait rehabilitation in Parkinson patients: a combined approach based on statistics and machine learning.
Donisi Leandro et al. Mathematical biosciences and engineering : MBE 2021 18(5) 6995-7009

Individually tailored self-management app-based intervention (selfBACK) versus a self-management web-based intervention (e-Help) or usual care in people with low back and neck pain referred to secondary care: protocol for a multiarm randomised clinical trial.
Marcuzzi Anna et al. BMJ open 2021 11(9) e047921

Predicting length of stay and mortality among hospitalized patients with type 2 diabetes mellitus and hypertension.
Barsasella Diana et al. International journal of medical informatics 2021 154104569

Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening.
Adams Johnathan W et al. IEEE journal of translational engineering in health and medicine 2021 94900907

Personalized Exercise Programs Based upon Remote Assessment of Motor Fitness: A Pilot Study among Healthy People Aged 65 Years and Older.
Netz Yael et al. Gerontology 2021 1-15

Ethical, Legal and Social Issues (ELSI)

Mind the Gaps: Ethical and Epistemic Issues in the Digital Mental Health Response to Covid-19.
Skorburg Joshua August et al. The Hastings Center report 2021

A Systematic Literature Review of Ethical Challenges Related to Medical and Public Health Data Sharing in China.
Li Xiaojie et al. Journal of empirical research on human research ethics : JERHRE 2021 15562646211040299

Health Equity in Artificial Intelligence and Primary Care Research: Protocol for a Scoping Review.
Wang Jonathan Xin et al. JMIR research protocols 2021 10(9) e27799

A Novel Imputation Approach for Sharing Protected Public Health Data.
Erdman Elizabeth A et al. American journal of public health 2021 e1-e9

Privacy and artificial intelligence: challenges for protecting health information in a new era.
Murdoch Blake et al. BMC medical ethics 2021 22(1) 122

General Practice

A review on deep learning in medical image analysis.
Suganyadevi S et al. International journal of multimedia information retrieval 2021 1-20

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.
Wang Zijian et al. Mathematical biosciences and engineering : MBE 2021 18(5) 6978-6994

Enhancing the value to users of machine learning-based clinical decision support tools: A framework for iterative, collaborative development and implementation.
Singer Sara J et al. Health care management review 2021

The future of digital health with federated learning.
Rieke Nicola et al. NPJ digital medicine 2021 3(1) 119

Clinical prediction models for hospital falls: a scoping review protocol.
Parsons Rex et al. BMJ open 2021 11(9) e051047

Preparing for the future: How organizations can prepare boards, leaders, and risk managers for artificial intelligence.
Dixit Arun et al. Healthcare management forum 2021 8404704211037995

Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial.
Wilson Patrick M et al. Trials 2021 22(1) 635

Privacy-preserving dataset combination and Lasso regression for healthcare predictions.
van Egmond Marie Beth et al. BMC medical informatics and decision making 2021 21(1) 266

Influential Usage of Big Data and Artificial Intelligence in Healthcare.
Yang Yan Cheng et al. Computational and mathematical methods in medicine 2021 20215812499

[Digitization of the healthcare system: the BfArM's contribution to the development of potential].
Broich Karl et al. Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz 2021

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes.
Ricard Benjamin Joseph et al. Journal of medical Internet research 2021 23(9) e27314

Comparing the predictive value of screening to the use of electronic health record data for detecting future suicidal thoughts and behavior in an urban pediatric emergency department: A preliminary analysis.
Haroz Emily E et al. Suicide & life-threatening behavior 2021

Identification of social determinants of health using multi-label classification of electronic health record clinical notes.
Stemerman Rachel et al. JAMIA open 2021 4(3) ooaa069

Social determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department.

Measuring the Value of a Practical Text Mining Approach to Identify Patients With Housing Issues in the Free-Text Notes in Electronic Health Record: Findings of a Retrospective Cohort Study.
Hatef Elham et al. Frontiers in public health 2021 9697501

Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers.

Heart, Lung, Blood and Sleep Diseases

American Heart Association Precision Medicine Platform Addresses Challenges in Data Sharing.
Stevens Laura M et al. Circulation. Cardiovascular quality and outcomes 2021 CIRCOUTCOMES121007949

Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records.
Guo Aixia et al. PloS one 2021 16(9) e0239007

Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning.
Kainz Bernhard et al. NPJ digital medicine 2021 4(1) 137

Infectious Diseases

Prediction of Multidrug-Resistant Tuberculosis Using Machine Learning Algorithms in SWAT, Pakistan.
Ali Mian Haider et al. Journal of healthcare engineering 2021 20212567080

Dengue models based on machine learning techniques: A systematic literature review.
Hoyos William et al. Artificial intelligence in medicine 2021 119102157

Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.
Forna Alpha et al. PloS one 2021 16(9) e0257005

Reproductive Health

Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review.
Chen Zhiyi et al. Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine 2021

Evaluation of artificial intelligence using time-lapse images of IVF embryos to predict live birth.
Sawada Yuki et al. Reproductive biomedicine online 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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