INTERVenE: Temporal-Abstraction-Interval Based Transformers for Short-Horizon Medical Event Prediction

arXiv:2608.29901 · cs.LG, cs.AI · Submitted 2026-08-30 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-30

Updated: 2026-08-30

Terminology

• Abstraction — 31× in this paper · explained in 1 episode
• Transformer — 20× in this paper · explained in 2 episodes
• Ablation — 14× in this paper · explained in 1 episode
• Interpretability — 12× in this paper · explained in 15 episodes
• Calibration — 9× in this paper · explained in 7 episodes
• Temporal Abstraction — 8× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Electronic Health Record — 4× in this paper
• Ontology — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Transformer Architecture — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Time2Vec: Learning a Vector Representation of Time
• Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

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