Last Posted: Oct 26, 2021
- A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset.
Donisi Leandro et al. Journal of imaging 2021 7(10)
- A language-matching model to improve equity and efficiency of COVID-19 contact tracing.
Lu Lisa et al. Proceedings of the National Academy of Sciences of the United States of America 2021 118(43)
- A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation.
Murdaca Giuseppe et al. Diagnostics (Basel, Switzerland) 2021 11(10)
- A Machine Learning Approach for Chronic Heart Failure Diagnosis.
Plati Dafni K et al. Diagnostics (Basel, Switzerland) 2021 11(10)
- Applications of Machine Learning in Bone and Mineral Research.
Kong Sung Hye et al. Endocrinology and metabolism (Seoul, Korea) 2021
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.
D'Antoni Federico et al. International journal of environmental research and public health 2021 18(20)
- Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters.
Sonnenschein Kristina et al. Biomedicines 2021 9(10)
- Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging.
Collins Toby et al. Diagnostics (Basel, Switzerland) 2021 11(10)
- Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?
Peng Tao et al. International journal of computer assisted radiology and surgery 2021
- Combined automated screening for age-related macular degeneration and diabetic retinopathy in primary care settings.
Bhuiyan Alauddin et al. Annals of eye science 2021 6
- Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure?
Lotierzo Manuela et al. Medicina (Kaunas, Lithuania) 2021 57(10)
- DermIA: Machine Learning to Improve Skin Cancer Screening.
Shoen Ezra et al. Journal of digital imaging 2021
- Identifying Actionability as a Key Factor for the Adoption of 'Intelligent' Systems for Drug Safety: Lessons Learned from a User-Centred Design Approach.
Gavriilidis George I et al. Drug safety 2021
- Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model.
Symum Hasan et al. Healthcare (Basel, Switzerland) 2021 9(10)
- Intelligent type 2 diabetes risk prediction from administrative claim data.
Uddin Shahadat et al. Informatics for health & social care 2021 1-15
- Machine Learning Algorithms to Predict Mortality of Neonates on Mechanical Intubation for Respiratory Failure.
Hsu Jen-Fu et al. Biomedicines 2021 9(10)
- Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma.
Semmler Georg et al. Journal of personalized medicine 2021 11(10)
- Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.
Lo Yu-Tai et al. BMC medical informatics and decision making 2021 21(1) 288
- Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.
Andaur Navarro Constanza L et al. BMJ (Clinical research ed.) 2021 375n2281
- The IDeaS initiative: pilot study to assess the impact of rare diseases on patients and healthcare systems.
Tisdale Ainslie et al. Orphanet journal of rare diseases 2021 16(1) 429
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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the 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 update, 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.