Summary of Assessing Foundation Models’ Transferability to Physiological Signals in Precision Medicine, by Matthias Christenson and Cove Geary and Brian Locke and Pranav Koirala and Warren Woodrich Pettine
Assessing Foundation Models’ Transferability to Physiological Signals in Precision Medicine
by Matthias Christenson, Cove Geary, Brian Locke, Pranav Koirala, Warren Woodrich Pettine
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a systematic pipeline for evaluating the transfer capabilities of foundation models in medical contexts. This pipeline leverages physiological simulation software to generate diverse, clinically relevant scenarios and project simulated signals through the foundation model to obtain embeddings. The embeddings are then evaluated using linear methods to quantify the model’s ability to capture critical aspects such as feature independence, temporal dynamics preservation, and scenario differentiation. Initial testing of the pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to help doctors make better decisions by understanding how different people’s bodies work. Right now, computers are good at recognizing patterns in data, but they’re not very good at handling the unique physiological signals of individual patients. This paper introduces a new way to test and fine-tune these computer models so that they can be used in hospitals and clinics. |
Keywords
» Artificial intelligence » Signal processing » Time series
