A Survey of Few-Shot Learning for Biomedical Time Series
<|reference_start|>A Survey of Few-Shot Learning for Biomedical Time Series: 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.<|reference_end|>
@article{li2024a,
    title={A Survey of Few-Shot Learning for Biomedical Time Series},
    author={Chenqi Li, Timothy Denison, Tingting Zhu},
    journal={arXiv preprint arXiv:2405.02485},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02485},
    primaryClass={cs.LG cs.AI}
}