School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Department of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Public Health. 2022 Dec 12;10:1053269. doi: 10.3389/fpubh.2022.1053269. eCollection 2022.
Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research.
This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research.
Research results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly.
The platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.
人工智能技术已成为医疗信息化发展的主流趋势。由于当前医疗信息化过程中结构复杂,产生了大量医疗数据,大数据技术辅助医生进行科研分析并获取高价值信息已成为医疗和科研不可或缺的手段。
本研究旨在通过分析医疗领域现有的数据挖掘方法和平台建设经验,利用基于 Hadoop 系统的大数据平台建设技术、模型预测以及基于统计学和机器学习原理的数据处理分析方法,探讨糖尿病智能数字平台的架构。我们提出了三大构建机制,即医疗数据集成和治理机制(DCM)、数据共享和隐私保护机制(DPM)以及医疗应用和医学研究机制(MCM),以打破传统医学研究和数字医学研究之间的障碍。此外,我们构建了一个高效便捷的智能糖尿病模型预测和数据分析平台,用于临床研究。
该平台的研究成果目前已应用于上海 T 医院的医学研究。在性能方面,平台运行流畅,能够实时处理大量医疗数据。在功能方面,数据采集、清洗和挖掘都集成到了系统中。通过简单直观的界面操作,可以方便快捷地处理和分析医疗和科研数据。
该平台可以作为医疗人员的辅助工具,促进医疗信息化和科研的发展。此外,该平台可能为未来医学提供基于证据的数字治疗和支持数字医疗服务的机会。