A Survey of Few-Shot Learning for Biomedical Time Series.

IF 17.2 1区 工程技术 Q1 ENGINEERING, BIOMEDICAL IEEE Reviews in Biomedical Engineering Pub Date : 2024-11-06 DOI:10.1109/RBME.2024.3492381
Chenqi Li, Timothy Denison, Tingting Zhu
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Abstract

Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care by improving long-term monitoring capabilities, facilitating early disease detection and intervention, as well as promoting personalized healthcare delivery. However, accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities to leverage past experiences to learn new tasks with limited examples, called few-shot learning. This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. The clinical benefits and limitations of such methods are discussed in relation to traditional data-driven approaches. This paper aims to provide insights into the current landscape of few-shot learning for biomedical time series and its implications for future research and applications.

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生物医学时间序列少点学习调查。
可穿戴传感器技术的进步和医疗记录的数字化促使生物医学时间序列数据空前普及。数据驱动的模型具有巨大的潜力,可以通过提高长期监测能力、促进早期疾病检测和干预以及促进个性化医疗服务来协助临床诊断和改善患者护理。然而,要获取广泛标注的数据集来训练对数据要求极高的深度学习模型,会遇到许多障碍,如罕见疾病的长尾分布、标注成本、隐私和安全问题、数据共享法规和伦理考虑等。克服标注数据稀缺问题的一种新兴方法是增强人工智能方法,使其具备类似人类的能力,利用过去的经验,在有限的示例中学习新任务,这就是所谓的 "少量学习"(few-shot learning)。本调查全面回顾和比较了生物医学时间序列应用中的少量学习方法。结合传统的数据驱动方法,讨论了这些方法的临床优势和局限性。本文旨在深入探讨生物医学时间序列少次学习的现状及其对未来研究和应用的影响。
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来源期刊
IEEE Reviews in Biomedical Engineering
IEEE Reviews in Biomedical Engineering Engineering-Biomedical Engineering
CiteScore
31.70
自引率
0.60%
发文量
93
期刊介绍: IEEE Reviews in Biomedical Engineering (RBME) serves as a platform to review the state-of-the-art and trends in the interdisciplinary field of biomedical engineering, which encompasses engineering, life sciences, and medicine. The journal aims to consolidate research and reviews for members of all IEEE societies interested in biomedical engineering. Recognizing the demand for comprehensive reviews among authors of various IEEE journals, RBME addresses this need by receiving, reviewing, and publishing scholarly works under one umbrella. It covers a broad spectrum, from historical to modern developments in biomedical engineering and the integration of technologies from various IEEE societies into the life sciences and medicine.
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