Summary of Comfort: a Continual Fine-tuning Framework For Foundation Models Targeted at Consumer Healthcare, by Chia-hao Li and Niraj K. Jha
COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare
by Chia-Hao Li, Niraj K. Jha
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 This paper proposes a novel framework called COMFORT for continual fine-tuning of Transformer-based foundation models in consumer healthcare. The authors integrate wearable medical sensors (WMSs) with machine learning to enable real-time monitoring and early-stage disease detection. Despite the success of Transformers, their application to sensitive domains like smart healthcare is underexplored due to data accessibility and privacy concerns. COMFORT pre-trains a Transformer-based foundation model on physiological signals from healthy individuals using masked data modeling (MDM). The authors then fine-tune the model using parameter-efficient fine-tuning (PEFT) methods, such as LoRA, for various disease detection tasks. COMFORT also constructs a library for multi-disease detection by storing low-rank decomposition matrices obtained from PEFT algorithms. Experimental results show that COMFORT achieves competitive performance while reducing memory overhead by up to 52% compared to conventional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about using special sensors and artificial intelligence (AI) to help people get medical check-ups earlier and more easily. The idea is to use wearable devices that can monitor our body signals in real-time, kind of like a smartwatch. This would allow doctors to detect diseases sooner, when they are easier to treat. The authors are trying to make this work by teaching AI models to learn from the data collected by these sensors. They want to make it possible for people to get diagnosed and treated earlier, which could lead to better health outcomes. |
Keywords
* Artificial intelligence * Fine tuning * Lora * Machine learning * Parameter efficient * Transformer
