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Assessing Foundation Models' Transferability to Physiological Signals in Precision Medicine

Matthias Christenson, Cove Geary, Brian Locke, Pranav Koirala, Warren Woodrich Pettine

TL;DR

The paper tackles the challenge of transferring foundation models to physiological signal analysis within precision medicine under data scarcity. It introduces a simulation-driven benchmarking pipeline that projects synthetic time-series through a foundation model and evaluates representations along feature disentanglement, temporal dynamics preservation, and scenario discrimination, followed by downstream task validation. Applying the Moirai time-series foundation model reveals systematic distortions in embeddings, including feature entanglement, poor signal reconstruction, and loss of temporal structure, leading to reduced clinical discriminability. These findings suggest that substantial architectural changes or targeted, synthetic-data–driven fine-tuning are required before such models can be deployed clinically, and the authors outline plans to broaden simulations and develop clinically relevant validation tasks and benchmarks.

Abstract

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our 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. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.

Assessing Foundation Models' Transferability to Physiological Signals in Precision Medicine

TL;DR

The paper tackles the challenge of transferring foundation models to physiological signal analysis within precision medicine under data scarcity. It introduces a simulation-driven benchmarking pipeline that projects synthetic time-series through a foundation model and evaluates representations along feature disentanglement, temporal dynamics preservation, and scenario discrimination, followed by downstream task validation. Applying the Moirai time-series foundation model reveals systematic distortions in embeddings, including feature entanglement, poor signal reconstruction, and loss of temporal structure, leading to reduced clinical discriminability. These findings suggest that substantial architectural changes or targeted, synthetic-data–driven fine-tuning are required before such models can be deployed clinically, and the authors outline plans to broaden simulations and develop clinically relevant validation tasks and benchmarks.

Abstract

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our 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. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.

Paper Structure

This paper contains 17 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Methods
  3. Creation of synthetic physiological signals for precision medicine.
  4. Projection into the foundation model embeddings.
  5. Assessing embeddings' representation of features, temporal dynamics and scenarios, then validating with a task.
  6. Results
  7. Foundation Model Embeddings Introduce Systematic Distortions to Physiological Signals
  8. Correlated noise is introduced through feature embeddings.
  9. Poor signal reconstruction from embeddings.
  10. Destruction of temporal dynamics.
  11. Reduced dimensionality between scenarios compromises scenario discrimination.
  12. Reduced capacity for feature decoding impairs clinical utility.
  13. Discussion
  14. Next steps in pipeline development.
  15. Targeted fine-tuning using distributions of simulated data.
  16. ...and 2 more sections

Figures (1)

  • Figure 1: Pipeline assessment of the Moirai Foundation Model. Feature codings: A=arterial pressure (mmHg); B=carbon dioxide production rate (mL/min); C=central venous pressure (mmHg); D=heart rate (1/min); E=oxygen consumption rate (mL/min); F=renal blood flow (L/min); G=respiration rate (1/min). (A) Overview of the pipeline. (B) Correlations among features within a scenario for a single scenario of raw data (left), embedded features (middle) and the distribution of correlations across the entire matrix (right). (C) Correlation of features for raw and embedded data for a single scenario (left) and across scenarios (right). (D) Temporal dynamics of raw and embedded data for a single session (left), the mean variance captured by PCA across sessions (middle) and the smoothness of trajectories across sessions (right). (E) Correlation among scenarios for raw data of a single scenario (top left), embedded data of a single scenario (bottom left), across scenarios (middle), and the dimensionality of the dynamics (right).