TREDL-Spec: Temporal Replicate-Aware Disentangling for Longitudinal Clinical Spectroscopy

Longitudinal spectroscopy can provide non-invasive monitoring of biological change, but repeated measurements introduce variability from both temporal state and measurement variation. Existing representation learning methods have separately explored static–temporal factorization, known relationships between observations, and measurement-specific variation, but these complementary sources of information have not been jointly leveraged to structure learned representations. This gap is particularly consequential for clinical spectroscopy, an increasingly explored but still comparatively underdeveloped area of biomedical AI, where substantial measurement variability can be difficult to distinguish from true biological change and can compromise the reliability and clinical trustworthiness of learned representations. We introduce TREDL-Spec (Temporal Replicate-Aware Disentangling for Longitudinal Spectroscopy), a replicate-aware framework that factorizes each spectrum into subject-stable, temporally evolving, and measurement-specific representations. Nested invariance constraints exploit repeated measurements (replicates) to explicitly associate these representations with their intended sources, while temporal dynamics are learned separately from measurement-specific nuisance variation. We evaluate the framework on controlled synthetic spectroscopy data and longitudinal human Raman spectroscopy data. TREDL-Spec achieved the highest SAP and DCI disentanglement on the synthetic dataset and the highest DCI on the human dataset, while its most predictive latent dimensions provided the strongest subject and temporal factor predictability on both datasets. TREDL-Spec also achieved the lowest future-spectrum prediction error across all evaluated metrics. These results demonstrate that explicitly structuring representations around biological and measurement-specific sources can support longitudinal spectral prediction while providing an explicit factorization of measurement variability.