{"title":"精准医疗中的医疗大数据存储:系统综述","authors":"Mostafa Langarizadeh, Mehdi Hajebrahimi","doi":"10.31661/jbpe.v0i0.2402-1730","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The characteristics of medical data in Precision Medicine (PM), the challenges related to their storage and retrieval, and the effective facilities to address these challenges are importantly considered in implementing PM. For this purpose, a secured and scalable infrastructure for various data integration and storage is needed.</p><p><strong>Objective: </strong>This study aimed to determine the characteristics of PM data and recognize the challenges and solutions related to appropriate infrastructure for data storage and its related issues.</p><p><strong>Material and methods: </strong>In this systematic study, coherent research was conducted on Web of Science, Scopus, PubMed, Embase, and Google Scholar from 2015 to 2023. A total of 16 articles were selected and evaluated based on the inclusion and exclusion criteria and the central search theme of the study.</p><p><strong>Results: </strong>A total of 1,961 studies were identified from designated databases, 16 articles met the eligibility criteria and were classified into five main sections PM data and its major characteristics based on the volume, variety and velocity (3Vs) of medical big data, data quality issues, appropriate infrastructure for PM data storage, cloud computing and PM infrastructure, and security and privacy. The variety of PM data is categorized into four major categories.</p><p><strong>Conclusion: </strong>A suitable infrastructure for precision medicine should be capable of integrating and storing heterogeneous data from diverse departments and sources. By leveraging big data management experiences from other industries and aligning their characteristics with those in precision medicine, it is possible to facilitate the implementation of precision medicine while avoiding duplication.</p>","PeriodicalId":38035,"journal":{"name":"Journal of Biomedical Physics and Engineering","volume":"15 3","pages":"205-220"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12153493/pdf/","citationCount":"0","resultStr":"{\"title\":\"Medical Big Data Storage in Precision Medicine: A Systematic Review.\",\"authors\":\"Mostafa Langarizadeh, Mehdi Hajebrahimi\",\"doi\":\"10.31661/jbpe.v0i0.2402-1730\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The characteristics of medical data in Precision Medicine (PM), the challenges related to their storage and retrieval, and the effective facilities to address these challenges are importantly considered in implementing PM. 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引用次数: 0
摘要
背景:精准医疗(PM)中医疗数据的特点、存储和检索相关的挑战以及应对这些挑战的有效设施是实施PM的重要考虑因素。为此,需要一个用于各种数据集成和存储的安全且可扩展的基础设施。目的:本研究旨在确定PM数据的特征,并认识到与数据存储的适当基础设施及其相关问题相关的挑战和解决方案。材料与方法:本系统研究在2015 - 2023年间对Web of Science、Scopus、PubMed、Embase、谷歌Scholar进行了连贯的研究。根据纳入和排除标准以及研究的中心检索主题,共选择并评估了16篇文章。结果:从指定的数据库中共识别出1961项研究,16篇文章符合资格标准,并根据医疗大数据的数量、种类和速度(3Vs)、数据质量问题、PM数据存储的适当基础设施、云计算和PM基础设施以及安全性和隐私性,将PM数据及其主要特征分为五个主要部分。PM数据的多样性被分为四大类。结论:适合精准医疗的基础设施应能够整合和存储来自不同科室和来源的异构数据。借鉴其他行业的大数据管理经验,结合精准医疗的特点,既能促进精准医疗的实施,又能避免重复。
Medical Big Data Storage in Precision Medicine: A Systematic Review.
Background: The characteristics of medical data in Precision Medicine (PM), the challenges related to their storage and retrieval, and the effective facilities to address these challenges are importantly considered in implementing PM. For this purpose, a secured and scalable infrastructure for various data integration and storage is needed.
Objective: This study aimed to determine the characteristics of PM data and recognize the challenges and solutions related to appropriate infrastructure for data storage and its related issues.
Material and methods: In this systematic study, coherent research was conducted on Web of Science, Scopus, PubMed, Embase, and Google Scholar from 2015 to 2023. A total of 16 articles were selected and evaluated based on the inclusion and exclusion criteria and the central search theme of the study.
Results: A total of 1,961 studies were identified from designated databases, 16 articles met the eligibility criteria and were classified into five main sections PM data and its major characteristics based on the volume, variety and velocity (3Vs) of medical big data, data quality issues, appropriate infrastructure for PM data storage, cloud computing and PM infrastructure, and security and privacy. The variety of PM data is categorized into four major categories.
Conclusion: A suitable infrastructure for precision medicine should be capable of integrating and storing heterogeneous data from diverse departments and sources. By leveraging big data management experiences from other industries and aligning their characteristics with those in precision medicine, it is possible to facilitate the implementation of precision medicine while avoiding duplication.
期刊介绍:
The Journal of Biomedical Physics and Engineering (JBPE) is a bimonthly peer-reviewed English-language journal that publishes high-quality basic sciences and clinical research (experimental or theoretical) broadly concerned with the relationship of physics to medicine and engineering.