Summary of A Survey Of Few-shot Learning For Biomedical Time Series, by Chenqi Li et al.
A Survey of Few-Shot Learning for Biomedical Time Series
by Chenqi Li, Timothy Denison, Tingting Zhu
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the potential of data-driven models in improving patient care through long-term monitoring, early disease detection, and personalized healthcare delivery. However, accessing labeled datasets is a significant barrier due to factors like rare diseases, annotation costs, privacy concerns, and regulations. To overcome this issue, few-shot learning methods can be used to leverage past experiences and learn new tasks with limited examples. The paper provides a comprehensive review of these methods for biomedical time series applications, discussing their clinical benefits and limitations compared to traditional data-driven approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can use machines to help doctors make better decisions by analyzing medical data from wearable devices and electronic health records. Right now, it’s hard to get the training data that deep learning models need because it’s expensive to label all the data, and there are concerns about privacy and sharing. To solve this problem, we’re looking at a new way of teaching machines called few-shot learning, where they can learn from just a few examples. This paper reviews the different ways we’re using this method in medicine and what it could do for patients. |
Keywords
» Artificial intelligence » Deep learning » Few shot » Time series
