Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content


Main|Search|PHGKB
Search PHGKB:

Last Posted: Apr 17, 2024
spot light Highlights

Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records
M Nigau et al, Nature Comm, March 7, 2024

From the abstract: "Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events. "

Combining rare and common genetic variants improves population risk stratification for breast cancer
A Bolze et al, Genetics in Medicine Open, February 2, 2024

From the abstract: " This study aimed to evaluate the performance of different genetic screening approaches to identify women at high-risk of breast cancer in the general population. We retrospectively studied 25,591 women with available electronic health records and genetic data, participants in the Healthy Nevada Project. Family history of breast cancer was ascertained on or after the record of breast cancer for 78% of women with both, indicating that this risk assessment method is not being properly utilized for early screening. Genetics offered an alternative method for risk assessment. 11.4% of women were identified as high-risk based on possessing a predicted loss-of-function (pLOF) variant in BRCA1, BRCA2 or PALB2 (hazard ratio = 10.4, 95% confidence interval: 8.1-13.5), or a pLOF variant in ATM or CHEK2 (HR = 3.4, CI: 2.4-4.8), or being in the top 10% of the polygenic risk score (PRS) distribution (HR = 2.4, CI: 2.0-2.8). "

Large language models to identify social determinants of health in electronic health records
M Guevera et al, NPJ Digital Medicine, January 11, 2023

From the abstract: "Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. "

Data-driven science and diversity in the All of Us Research Program.
Geoffrey S Ginsburg et al. Sci Transl Med 2023 12 (726) eade9214

From the paper: "Having >1 million whole-genome sequences integrated with longitudinal data from questionnaires and electronic health records will allow a comprehensive molecular epidemiological approach across the life span. Genetic, environmental, and lifestyle data will be integrated and accessible, promoting an understanding of how their interactions drive transitions from health to disease and enabling a robust assessment of vulnerabilities and resilience for an individual or population. "

Search Result Summary


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.

TOP