A Survey of Few-Shot Learning for Biomedical Time Series

• URL: http://arxiv.org/abs/2405.02485v1
• Date: Fri, 3 May 2024 21:22:27 GMT
• Title: A Survey of Few-Shot Learning for Biomedical Time Series
• Authors: Chenqi Li, Timothy Denison, Tingting Zhu,
• Abstract summary: Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities 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.
• Score: 3.845248204742053
• License: http://creativecommons.org/licenses/by/4.0/
• 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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