Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether similar gains extend to multimodal, multivariate, and even simple univariate medical time series. Our objective is to assess the impact of SPT on the performance and scalability of transformer-based models across diverse medical applications, particularly under limited data conditions. We evaluate transformer architectures on three representative medic

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ethics.ai (6 August 2026), “Is Self-Pretraining really useful to improve diagnosis in medical Time Series?,” evidence record 17349, https://ethics.ai/record/17349 (originally published by arXiv cs.AI).

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