نوع مقاله : مقاله مروری نظام مند
نویسندگان
1 دانشجوی دکتری تخصصی، مهندسی نرمافزار، گروه مهندسی کامپیوتر، واحد ارومیه، دانشگاه آزاد اسلامی، ارومیه، ایران
2 استادیار، مهندسی نرمافزار، گروه مهندسی کامپیوتر، واحد ارومیه، دانشگاه آزاد اسلامی،ارومیه، ایران
3 استادیار، مهندسی نرمافزار، گروه مهندسی کامپیوتر، واحد ارومیه، دانشگاه آزاد اسلامی، ارومیه و پزشکی اجتماعی، گروه پزشکی اجتماعی، دانشکده پزشکی، دانشگاه علوم پزشکی تبریز، تبریز، ایران
چکیده
مقدمه: دادهکاوی، ابزار کارامدی جهت آشکارسازی دانش نهفته در کلاندادههای پزشکی میباشد. اولین قدم دادهکاوی، شناخت داده و چالشهای آن است. هدف از انجام پژوهش حاضر، بررسی سرمنشأ، تأثیرات و راهکارهای مواجهه با چالشهای کاوش کلاندادههای پزشکی و همچنین، تعیین منافع حاصل از کاوش بود.روش بررسی: در این تحقیق مروری، مطالعات انگلیسی با دو گروه کلید واژه مجزا برای مزایا و چالشها از پایگاههای اطلاعاتی PubMed، ScienceDirect، Springer و Google Scholar، طی بازه زمانی سالهای 2011 تا 2020 جستجو شد. مطالعات تک منظوره حذف و مطالعاتی که به صورت جامع کاوش کلاندادههای پزشکی را مورد بررسی قرار داده بودند، انتخاب شد. سپس هر چالش مورد بررسی دقیقتر قرار گرفت و نتایج به صورت طبقهبندی شده ارایه گردید.یافتهها: دانش حاصل از کاوش کلانداده پزشکی، سبب افزایش کیفیت ارایه خدمات درمانی میشود، اما خطا در جمعآوری و ثبت اطلاعات، ویژگیهای ناشی از کلانداده بودن و ساختار ذاتی دادههای پزشکی، چالشهای بسیاری بر سر راه کاوش قرار داده است که از بین آنها، «ناسازگاری، صحت، امنیت و محرمانگی داده»، دشوارترین مشکلات به شمار میروند. استانداردسازی و افزایش دقت و امنیت در جمعآوری، ذخیرهسازی و نمایش دادهها، مؤثرترین راهکارهای پیشگیری میباشد. طراحی و استفاده از بسترها، الگوریتمها و ساختارهای مناسب کلانداده و همچنین، بهرهگیری از روشهای یادگیری ماشین و هوش مصنوعی، راهکارهای مناسبی برای مواجهه با چالشها محسوب میشوند.نتیجهگیری: عدم آمادگی برای ظهور کلاندادههای پزشکی و رشد بسیار سریع آنها، سرمنشأ بروز چالشهایی برای الگوریتمهای کاوش هستند که برخی قابل پیشگیری، شناسایی و رفع میباشند و برخی نیز به روشهای هوشمند نوینی نیاز دارند که قابلیت مدیریت کلاندادههای پزشکی را داشته باشند.
کلیدواژهها
عنوان مقاله [English]
Advantages and Challenges of Medical Big Data Mining
نویسندگان [English]
- Leila Baradaran-Sorkhabi 1
- Farhad Soleimanian-Gharehchopogh 2
- Jafar Shahmfar 3
1 PhD Student, Software Engineering, Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
2 Assistant Professor, Software Engineering, Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran
3 Assistant Professor, Software Engineering, Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia AND Department of Community Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
چکیده [English]
Introduction: Data mining seems to be a good tool for showing underlying knowledge of Medical Big Data (MBD). Understanding characteristics of data and possible challenges are the first steps of the journey. This study endeavors to inspect reasons, effects, and solutions of challenges as well as benefits of MBD mining.Methods: In so doing, PubMed, ScienceDirect, Springer, and Google Scholar databases were scrutinized using two groups of keywords for benefits and challenges in the years 2011-202. The search language was English. Single-purpose studies were excluded and those studies that were focused on MBD mining were included. Then, challenge was examined separately and the results were categorized.Results: Extracted knowledge from MBD enhances quality of care. However, low-quality performance in gathering and storing the data, properties of big data, and inherent structure of medical data cause many problems for mining methods. Inconsistency, veracity, privacy, and security issues are the major challenging problems. Standardization and enhancing quality of data gathering, storing, and representing tasks are the effective problem prevention strategies. Designing and using appropriate frameworks, algorithms, and structures as well as utilizing machine learning and artificial intelligence techniques are the most effective solutions for dealing with the challenges.Conclusion: MBD was appeared and expanded when the world was not ready for it. Thus, it caused many challenges for mining methods. Some of them are traceable, preventable, and manageable. However, some challenges need novel and intelligent methods that are able to handle MBD.
کلیدواژهها [English]
- Data Mining
- Big Data
- Health
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