On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series
arXiv:2605.26194v1 Announce Type: new Abstract: Clinical time-series learning is routinely constrained by small, heterogeneous cohorts and protocol drift, while its downstream use spans both classification (e.g., pathology diagnosis) and regression (e.g., temporal forecasting). These constraints make foundation-model pretraining appealing, but raises an important question of which inductive biases should the pretraining objective impose so that representations transfer across task types and subjects. We study this question in pathological gait analysis for spinal cord injury (SCI) via PathoFM,
The proliferation of AI in healthcare demands robust pretraining methods for heterogeneous and limited clinical datasets, necessitating research into optimal inductive biases.
Achieving generalizable representations in clinical AI can unlock widespread foundation model applications across diverse medical tasks, significantly accelerating diagnostic and prognostic capabilities.
The understanding of how to design pretraining objectives for clinical time-series data improves, potentially leading to more effective and versatile AI models in healthcare.
- · AI in healthcare sector
- · Patients with complex conditions
- · Medical research institutions
- · Foundation model developers
- · Developers of custom-trained, task-specific clinical AI models
- · Methods reliant on extremely large, homogenous clinical datasets
Improved accuracy and efficiency in clinical diagnostics and prognostics using AI.
Reduced barriers to entry for AI deployment in healthcare, even for rare conditions with limited data.
Enhanced personalized medicine approaches, driven by more adaptable and robust AI models.
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Read at arXiv cs.LG