编辑本段 深度学习驱动的生物医学应用和数字医疗大数据分析

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-08-08 DOI:10.1109/TCBB.2024.3371808
Xiaokang Zhou;Carson K. Leung;Kevin I-Kai Wang;Giancarlo Fortino
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引用次数: 0

摘要

深度学习和大数据分析是生物医学应用和数字医疗领域最重要的研究课题之一。随着人工智能(AI)和物联网(IoT)技术的快速发展,用于大数据分析的深度学习(DL)--包括情感学习、强化学习和迁移学习--被广泛应用于人类健康的感知、学习和交互。生物医学应用的例子包括智能生物材料、生物医学成像、心跳/血压测量和眼球跟踪。这些生物医学应用通过远程传感器收集医疗保健数据,并将数据传输到中央系统进行分析。面对海量的历史数据,DL 和大数据分析技术能够识别特征与可能风险之间的潜在联系,提出重要的医疗诊断决策,并为更好的医疗治疗和生活方式提供宝贵建议。尽管人工智能、数字图书馆和大数据分析技术在医疗保健研究方面取得了重大进展,但计算机辅助治疗设计与现实世界的医疗保健需求之间仍存在差距。此外,在医疗保健和生物医学应用领域,前沿的人工智能和 DL 技术还有一些尚未开发的领域。因此,探索 DL 和大数据分析在生物医学应用和数字医疗领域的可能性是非常有必要的。
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Editorial Deep Learning-Empowered Big Data Analytics in Biomedical Applications and Digital Healthcare
Deep learning and big data analysis are among the most important research topics in the fields of biomedical applications and digital healthcare. With the fast development of artificial intelligence (AI) and Internets of Things (IoT) technologies, deep learning (DL) for big data analytics—including affective learning, reinforcement learning, and transfer learning—are widely applied to sense, learn, and interact with human health. Examples of biomedical applications include smart biomaterials, biomedical imaging, heartbeat/blood pressure measurement, and eye tracking. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. With an enormous amount of historical data, DL and big data analysis technologies are able to identify potential linkage between features and possible risks, raise important decision for medical diagnosis, and provide precious advice for better healthcare treatment and lifestyle. Although significant progress has been made with AI, DL, and big data analytic technologies for medical and healthcare research, there remain gaps between the computer-aided treatment design and real-world healthcare demands. In addition, there are unexplored areas in the fields of healthcare and biomedical applications with cutting-edge AI and DL technologies. Hence, exploring the possibility of DL and big data analytics in the fields of biomedical applications and digital healthcare is in high demand.
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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