Enhancing Healthcare Analytics and Accelerating Personalized Treatment through Comparative Studies of High-Throughput Database Architectures

Abdelrahman Freek ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 7, issue 01, 120-139

Abstract: This study looks at the transformative role of high-throughput database architectures in advancing healthcare analytics and accelerating personalized treatment. The study explores various database frameworks, including distributed SQL, NoSQL, and specialized analytical platforms, to find the best option for handling medical data that is growing very fast. The paper discusses, through a comparative analysis, the speed of ingestion, query performance, scalability, fault tolerance, and data integration for applicability in modern healthcare needs. These insights are expected to help healthcare organizations in the selection and deployment of database systems that enhance data-driven decision-making and, in turn, improve the outcomes of patient care. The findings contribute to the broader discourse on integrating advanced technologies in personalized medicine, with efficient database systems playing a pivotal role.

Keywords: Health analytics; Personalized medicine; High-throughput databases; Distributed SQL; NoSQL; Data integration; Scalability; Fault tolerance; medical data (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:7:y:2024:i:01:p:120-139:id:303

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