AI Article Synopsis
- Electronic Health Records (EHRs) are vital for biomedical research as they offer comprehensive patient data, which can enhance the understanding of real-world medical scenarios when integrated across different clinical sites.* -
- The study utilized a specialized algorithm called the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to analyze data while maintaining patient privacy, showcasing its practicality within large research networks.* -
- The research focused on linking clinical risk factors to the onset of Alzheimer's disease and related dementias (ADRD), aiming to deepen insights into these conditions and improve patient care.*
Article Abstract
With vast amounts ofpatients' medical information, electronic health records (EHRs) are becoming one of the most important data sources in biomedical and health care research. Effectively integrating data from multiple clinical sites can help provide more generalized real-world evidence that is clinically meaningful. To analyze the clinical data from multiple sites, distributed algorithms are developed to protect patient privacy without sharing individual-level medical information. In this paper, we applied the One-shot Distributed Algorithm for Cox proportional hazard model (ODAC) to the longitudinal data from the OneFlorida Clinical Research Consortium to demonstrate the feasibility of implementing the distributed algorithms in large research networks. We studied the associations between the clinical risk factors and Alzheimer's disease and related dementia (ADRD) onsets to advance clinical research on our understanding of the complex risk factors of ADRD and ultimately improve the care of ADRD patients.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075520 | PMC |
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