arXiv:2607. 21573v1 Announce Type: cross Abstract: Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it.
By Hongnan Ma, Yiwei Shi, Mengyue Yang, Weiru Liu
arXiv:2608. 01587v1 Announce Type: cross Abstract: Machine-learning benchmarks often pair a label that aggregates a long temporal horizon with input observed through one or a few short windows.
By Xizhe Zhang
The paper introduces CALIBRA, a calibration-first multimodal temporal learning framework designed for transferable asthma‑risk forecasting across varying patient cohorts and sensor ecosystems. It processes multiple data streams—environmental, pulmonary, symptom, medication, wearable, and context—using dedicated recurrent encoders, a reliability‑conditioned gate, and gradient‑reversal training to mitigate cohort bias. CALIBRA employs a shrinkage‑based hierarchical logistic layer for probability calibration and split conformal prediction for abstention‑capable prediction sets, achieving competitive performance on a semi‑synthetic three‑cohort benchmark with controlled distribution shift.
By Taimoor Ahmad
arXiv:2607. 19020v1 Announce Type: cross Abstract: Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice.
By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud
arXiv:2608.29419v1 Announce Type: cross
Abstract: Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over s...
By Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell, Aseel S. Abuzour, Michael Abaho, Danushka Bollegala, Gary Leeming, Eduard Shantsila, Andrew Clegg, Lauren E. Walker, Iain Edward Buchan, Samuel D. Relton
arXiv:2606. 10678v1 Announce Type: new Abstract: Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies.
By Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers, M. M. Lutfe Elahi, Sifat Momen, Nabeel Mohammed, Shafin Rahman