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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 04/27/2023

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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Birth Defects and Child Health

Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images.
Xiong Chen et al. iScience 2023 26(4) 106456

Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images.
Hiroki Den et al. Sci Rep 13(1) 6693

Cancer

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images.
Parehe Alimu et al. Quant Imaging Med Surg 2023 13(4) 2675-2687

Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning.
Chenhua Luo et al. Front Neurol 2023 141100933

Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study using Basic Patient Demographic, Clinical, and Surgical Inputs.
Marc Ghanem et al. World Neurosurg

Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre, randomised de-escalation trial.
Benjamin H Kann et al. Lancet Digit Health

Big Data, Machine Learning, and Artificial Intelligence to Advance Cancer Care: Opportunities and Challenges.
Andreas Charalambous et al. Semin Oncol Nurs 151429

Predictive power of deep-learning segmentation based prognostication model in non-small cell lung cancer.
Jordan C Gainey et al. Front Oncol 13868471

Application of artificial intelligence for overall survival risk stratification in oropharyngeal carcinoma: A validation of ProgTOOL.
Rasheed Omobolaji Alabi et al. Int J Med Inform 175105064

Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.
Olutomilayo Olayemi Petinrin et al. Comput Struct Biotechnol J 212454-2470

Study on diagnosis of thyroid nodules based on convolutional neural network.
AiTao Yin et al. Radiologie (Heidelb)

Machine-learning predicts time-series prognosis factors in metastatic prostate cancer patients treated with androgen deprivation therapy.
Shinpei Saito et al. Sci Rep 13(1) 6325

Chronic Disease

Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost.
Tingting Fan et al. Front Public Health 111087297

Interpretable Machine Learning for Fall Prediction among Older Adults in China.
Xiaodong Chen et al. Am J Prev Med

Economic Evaluation of Artificial Intelligence Systems Versus Manual Screening for Diabetic Retinopathy in the United States.
Harshvardhan Chawla et al. Ophthalmic Surg Lasers Imaging Retina 1-9

Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study.
Fengda Li et al. Front Neurol 141139096

Ethical, Legal and Social Issues (ELSI)

Using the Veil of Ignorance to align AI systems with principles of justice.
Laura Weidinger et al. Proc Natl Acad Sci U S A 120(18) e2213709120

More Process, Less Principles: The Ethics of Deploying AI and Robotics in Medicine.
Amitabha Palmer et al. Camb Q Healthc Ethics 1-14

More than algorithms: an analysis of safety events involving ML-enabled medical devices reported to the FDA.
David Lyell et al. J Am Med Inform Assoc

General Practice

Diagnostic medical artificial intelligence: Futuristic prospects for implementation in healthcare settings.
Vishruth M Nagam et al. Front Artif Intell 2023 61169244

Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment.
Matthew Squires et al. Brain Inform 10(1) 10

Commentary: Integrated blockchain-deep learning approach for analyzing the electronic health records recommender system.
Siwan Noh et al. Front Public Health 111133142

Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms.
Roghayyeh Hassanzadeh et al. BMC Med Res Methodol 23(1) 101

Exploring Stakeholder Requirements to Enable Research and Development of Artificial Intelligence Algorithms in a Hospital-Based Generic Infrastructure: Results of a Multistep Mixed Methods Study.
Lina Weinert et al. JMIR Form Res 2023 7e43958

Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm.
Stephen Chi et al. JAMA Netw Open 2023 6(4) e238795

Laboratory Data Quality Evaluation in the Big Data Era.
Sollip Kim et al. Ann Lab Med 43(5) 399-400

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.
Majid Afshar et al. JMIR Med Inform 11e44977

The Therapeutic Applications of Machine Learning in Atopic Dermatitis: A Scoping Review.
Eric P McMullen et al. J Cutan Med Surg 12034754231168846

Geolocation Patterns, Wi-Fi Connectivity Rates, and Psychiatric Symptoms Among Urban Homeless Youth: Mixed Methods Study Using Self-report and Smartphone Data.
Yousaf Ilyas et al. JMIR Form Res 2023 7e45309

Heart, Lung, Blood and Sleep Diseases

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review.
Sobhan Moazemi et al. Front Med (Lausanne) 2023 101109411

Artificial Intelligence in Cardiology: Applications and Obstacles.
Alexandrina Danilov et al. Curr Probl Cardiol 101750

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation.
Nilakash Das et al. Eur Respir J

Positional sleep apnea phenotyping using machine learning and digital oximetry biomarkers.
Yuval Ben Sason et al. Physiol Meas

Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure.
Zijun Chen et al. Front Cardiovasc Med 101119699

Machine learning prediction of mortality in Acute Myocardial Infarction.
Mariana Oliveira et al. BMC Med Inform Decis Mak 23(1) 70

Infectious Diseases

Added value of chest CT in a machine learning-based prediction model to rule out COVID-19 before inpatient admission: A retrospective university network study.
Martin Krämer et al. Eur J Radiol 163110827

Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022.
Daniel Niguse Mamo et al. BMC Med Inform Decis Mak 23(1) 75


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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