医学AI新模型TSPFN来了，专门处理生理时间序列，比TabPFN和专用模型表现更好。
TSPFN是专为生理时间序列分类设计的基础模型，重构了TabPFN架构以捕捉时间依赖性。该模型在140,000个多医学领域的生理时间序列上预训练，在多个生理学基准测试中表现优于标准表格基线和TabPFN。相比专业深度时间序列模型，TSPFN展现出更好的跨领域泛化能力。
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Designing models that generalize effectively in low- to medium-data regimes remains a primary challenge in medical machine learning, particularly for physiological time-series classification. While tabular foundation models such as TabPFN offer an attractive alternative to conventional fine-tuning through in-context learning, they are not designed to capture the temporal dependencies inherent to physiological signals. ~In this paper, we introduce TSPFN, a foundation model that redesigns TabPFN's architecture for time series data. TSPFN integrates structured temporal representations and positional embeddings to capture intra-sample temporal and channel dependencies. To fully leverage its spatio-temporal design, the model is pretrained on 140,000 real-world physiological time series across multiple medical domains. This yields a unified, generalizable framework capable of learning the specificities of medical time series. Experiments across diverse physiological benchmarks demonstrate that TSPFN consistently outperforms standard tabular baselines and TabPFN, and achieves superior cross-domain generalization compared to specialized deep time-series models. All our experiments, ablation studies, and pre-processing scheme are publicly available at https://github.com/Jeremstym/TSPFN