Patient-centric repositories of health records are an important component of health information infrastructure. However, patient information in a single repository is potentially vulnerable to loss of the entire dataset from a single unauthorized intrusion. A new health record storage architecture, the personal grid, eliminates this risk by separately storing and encrypting each person's record. The tradeoff for this improved security is that a personal grid repository must be sequentially searched since each record must be individually accessed and decrypted. To allow reasonable search times for large numbers of records, parallel processing with hundreds (or even thousands) of on-demand virtual servers (now available in cloud computing environments) is used. Estimated search times for a 10 million record personal grid using 500 servers vary from 7 to 33min depending on the complexity of the query. Since extremely rapid searching is not a critical requirement of health information infrastructure, the personal grid may provide a practical and useful alternative architecture that eliminates the large-scale security vulnerabilities of traditional databases by sacrificing unnecessary searching speed.
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http://dx.doi.org/10.1016/j.jbi.2016.04.004 | DOI Listing |
Sci Rep
January 2025
Hospital Tuanku Ja'afar, Jalan Rasah, 70300, Seremban, Negeri Sembilan, Malaysia.
The COVID-19 pandemic has burdened healthcare systems globally. To curb high hospital admission rates, only patients with genuine medical needs are admitted. However, machine learning (ML) models to predict COVID-19 hospitalization in Asian children are lacking.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh, Kingdom of Saudi Arabia.
Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded by more sophisticated approaches.
View Article and Find Full Text PDFVaccines (Basel)
January 2025
Infectious Diseases Research Center of Niigata University in Myanmar, Niigata University, Niigata 950-8510, Japan.
Background: This study aimed to assess the antibody response to SARS-CoV-2 vaccines among healthcare workers (HCWs) from multiple outpatient clinics in Japan, examining the effects of baseline characteristics (e.g., sex, age, underlying condition, smoking history, occupation) and prior infections.
View Article and Find Full Text PDFMol Breed
February 2025
National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024 China.
Unlabelled: Chickpea (. L) holds the esteemed position of being the second most cultivated and consumed legume crop globally. Nevertheless, both biotic and abiotic constraints limit chickpea production.
View Article and Find Full Text PDFJ Diabetes Metab Disord
June 2025
Department of Peripheral Vascular Diseases, First Affiliated Hospital, Heilongjiang University of Traditional Chinese Medicine, Harbin, China.
Objective: The escalating prevalence of Type-2 diabetes mellitus (T2DM) poses a significant global health challenge. Utilizing integrative proteomic analysis, this study aimed to identify a panel of potential protein markers for T2DM, enhancing diagnostic accuracy and paving the way for personalized treatment strategies.
Methods: Proteome profiles from two independent cohorts were integrated: cohort 1 composed of 10 T2DM patients and 10 healthy controls (HC), and cohort 2 comprising 87 T2DM patients and 60 healthy controls.
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