arXiv:2607. 14733v1 Announce Type: new Abstract: Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or inhibit one another in ways that snapshot-level models cannot express; and (iii) inter-arrival times are heavy-tailed and statistically sparse, so deterministic time predictors are unreliable.
By Xiangni Tian, Kaixian Yu, Runpeng Dai, Niansheng Tang, Hongtu Zhu
INTERVenE introduces Transformer models that use a knowledge‑based temporal abstraction (KBTA) token stream of named clinical concepts instead of raw measurements, enabling per‑token attributions to resolve directly to clinical concepts. Two variants are offered: an auto‑regressive decoder that generates future abstraction trajectories with step‑wise risk readouts, and a bidirectional encoder that jointly predicts risk and time‑to‑event in a single pass. On 57,078 MIMIC‑IV admissions, the encoder variant outperforms neural baselines with a support‑weighted AUPRC of 0.672 and AUROC of 0.901, while the decoder provides complementary token‑level risk trajectories.
By Shahar Oded, Yuval Shahar
arXiv:2607. 11177v1 Announce Type: new Abstract: In this paper, we propose deep learning based NeuroMem-FHP framework for estimating the parameters of the fractional Hawkes process (FHP), a self-exciting point process that captures long-range dependence through a fractional Mittag-Leffler excitation kernel.
By Neha Gupta, Aditya Maheshwari
arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.
By Zilin Jing, Vincent Jeanselme, Yuta Kobayashi, Simon A. Lee, Chao Pang, Aparajita Kashyap, Yanwei Li, Xinzhuo Jiang, Shalmali Joshi
arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.
By Jingteng Li, Alexander Capstick, Louise Rigny, Iona Biggart, Neil J Sebire, Payam Barnaghi
arXiv:2509. 21996v3 Announce Type: replace-cross Abstract: Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering structure.
By Trinnhallen Brisley, Gordon Ross, Daniel Paulin
arXiv:2607. 01022v1 Announce Type: new Abstract: Spatiotemporal point processes (STPPs) model event data in continuous time and space, with applications in mobility, epidemiology, and public safety.
By Yahya Aalaila, Gerrit Gro{\ss}mann, Sebastian Vollmer
The study benchmarks tokenization choices for generative medical event models, evaluating quantization granularity, reference-range anchoring, code–value fusion, numeric and temporal encodings, and native versus harmonized event representations. Using Llama and Qwen architectures, 156 models were trained and assessed on early hospitalization data, showing that fusing codes with value deciles and using event-order or admission-relative RoPE embeddings improved predictive performance. The Common Longitudinal Intensive Care Unit Data Format (CLIF) reduced token count by 30.8% while enhancing outcomes in most families.
By Inhyeok Lee, Luke Solo, Michael C. Burkhart, Bashar Ramadan, Sahil Sethi, Sarah Jabbour, William F. Parker, Brett K. Beaulieu-Jones
arXiv:2608. 06122v1 Announce Type: cross Abstract: 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.
By Omar Coser, Antonio Orvieto, Paolo Soda, Loredana Zollo
arXiv:2509. 24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future events.
By David Berghaus, Patrick Seifner, Kostadin Cvejoski, C\'esar Ojeda, Rams\'es J. S\'anchez
arXiv:2605.20292v2 Announce Type: replace
Abstract: Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns s...
By Kwanhyung Lee, Juhwan Choi, Jongheon Kim, Joohyung Lee, Hyeongwon Jang, Jeonguk Lee, Jisoo Jung, Eunho Yang
arXiv:2607. 21098v1 Announce Type: new Abstract: Temporal point processes (TPPs) provide a general and flexible framework for modeling sequences of events in continuous time.
By Michele Bellomo, Riccardo Ramaschi, Alberto Dolara, Tomaso Aste