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
Non-Genomics Precision Health Scan
Annual Research Review: Translational machine learning for child and adolescent psychiatry.
Dwyer Dominic et al. Journal of child psychology and psychiatry, and allied disciplines 2022
Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis.
Dercle Laurent et al. JAMA oncology 2022
Analysis of Routine Computed Tomographic Scans With Radiomics and Machine Learning: One Step Closer to Clinical Practice.
Farwell Michael D et al. JAMA oncology 2022
Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models.
Loraksa Chanunya et al. Journal of imaging 2022 8(1)
Ultrasonic Intelligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning.
Zhou Heng et al. Journal of healthcare engineering 2022 20226428796
Early dietitian referral in lung cancer: use of machine learning.
Chung Michael et al. BMJ supportive & palliative care 2022
Predictors of underutilization of lung cancer screening: a machine learning approach.
Guo Yuqi et al. European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP) 2022
Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer.
Wu Xing Long et al. Physics in medicine and biology 2022
Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT.
Jiang Beibei et al. Radiology 2022 210551
Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data.
He Peijuan et al. Journal of healthcare engineering 2022 20229506915
ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions.
Lucieri Adriano et al. Computer methods and programs in biomedicine 2022 215106620
Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer.
Han Tenghui et al. Cancer cell international 2022 22(1) 28
A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: The Ravansar county anthropometric cohort study.
Jafari Habib et al. PloS one 2022 17(1) e0262701
Digital Ageism: Challenges and Opportunities in Artificial Intelligence for Older Adults.
Chu Charlene H et al. The Gerontologist 2022
Efficacy and Applications of Artificial Intelligence and Machine Learning Analyses in Total Joint Arthroplasty: A Call for Improved Reporting.
Polce Evan M et al. The Journal of bone and joint surgery. American volume 2022
Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention.
Camp Edward J et al. Epilepsy & behavior : E&B 2022 127108548
Integrating predictive models into care: facilitating informed decision-making and communicating equity issues.
Nong Paige et al. The American journal of managed care 2022 28(1) 18-24
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning.
McCradden Melissa D et al. The American journal of bioethics : AJOB 2022 1-15
Ethical Considerations Related to Using Machine Learning-Based Prediction of Mortality in the Pediatric Intensive Care Unit.
Michelson Kelly N et al. The Journal of pediatrics 2022
Big Data and Complex Data Analytics: Breaking Peer Review?
Schwendicke F et al. Journal of dental research 2022 220345211070983
Standardized Health data and Research Exchange (SHaRE): promoting a learning health system.
Davis Sierra et al. JAMIA open 2022 5(1) ooab120
A decision support system for primary headache developed through machine learning.
Liu Fangfang et al. PeerJ 2022 10e12743
Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage.
Chen Wei et al. Frontiers in neurology 2022 12790682
Engagement With a Mobile Phone-Based Life Skills Intervention for Adolescents and Its Association With Participant Characteristics and Outcomes: Tree-Based Analysis.
Paz Castro Raquel et al. Journal of medical Internet research 2022 24(1) e28638
A machine learning approach to predict e-cigarette use and dependence among Ontario youth.
Shi Jiamin et al. Health promotion and chronic disease prevention in Canada : research, policy and practice 2022 42(1) 21-28
Evaluating Pointwise Reliability of Machine Learning prediction.
Nicora Giovanna et al. Journal of biomedical informatics 2022 103996
Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk.
Shakeri Hossein Abad Zahra et al. Journal of medical Internet research 2022 24(1) e28749
Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud.
Lee Kyu Hee et al. Yonsei medical journal 2022 63(Suppl) S84-S92
Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.
Kumar Yogesh et al. Journal of ambient intelligence and humanized computing 2022 1-28
Explainable deep learning in healthcare: A methodological survey from an attribution view.
Jin Di et al. WIREs mechanisms of disease 2022 e1548
Automatic emotion recognition in healthcare data using supervised machine learning.
Azam Nazish et al. PeerJ. Computer science 2022 7e751
A pilot study of a deep learning approach to detect marginal bone loss around implants.
Liu Min et al. BMC oral health 2022 22(1) 11
Deep Learning Algorithm Trained on Brain Magnetic Resonance Images and Clinical Data to Predict Motor Outcomes of Patients With Corona Radiata Infarct.
Kim Jeoung Kun et al. Frontiers in neuroscience 2022 15795553
Point-of-care artificial intelligence-enabled ECG for dyskalemia: a retrospective cohort analysis for accuracy and outcome prediction.
Lin Chin et al. NPJ digital medicine 2022 5(1) 8
Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View.
Naseri Jahfari Arman et al. JMIR medical informatics 2022 10(1) e29434
A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study.
Hwang Jeonghwan et al. Journal of medical Internet research 2022 24(1) e28659
Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review.
Suri Jasjit S et al. Computers in biology and medicine 2022 142105204
Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome.
Ke Jun et al. The American journal of emergency medicine 2022 53127-134
Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US.
Zhou Xingzuo et al. Human vaccines & immunotherapeutics 2022 1-8
The importance of association of comorbidities on COVID-19 outcomes: a machine learning approach.
Arévalo-Lorido José Carlos et al. Current medical research and opinion 2022 1-32
Prediction and Evaluation of healthy and unhealthy status of COVID-19 patients using wearable device prototype data.
Hussain Shaik Asif et al. MethodsX 2022 101618
Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.
Huang Bo et al. BMC pregnancy and childbirth 2022 22(1) 36
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.
- Page last reviewed:Feb 1, 2024
- Page last updated:Mar 28, 2024
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