arXiv:2608.24688v1 Announce Type: new
Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal ob...
By Eugene Vorontsov, Yi Kan Wang, Alican Bozkurt, Adam Casson, Ludmila Tydlitatova, Michal Zelechowski, Ezra E. W. Cohen, Jyoti D. Patel, Max Banaszak, Caitlin McWilliams, Shane Colley, Kate Sasser, Ryan Fukushima, Eric Lefkofsky, Razik Yousfi, Siqi Liu
arXiv:2610.33054v1 Announce Type: new
Abstract: Simulating an individual's disease course requires connecting patient history to future events, clinical timing and disease morphology. Longitudinal pr...
By Youcheng Li (Peking University, Isoplex Lab), Fa Tian (Isoplex Lab), Binbin Su (Peking Union Medical College, Isoplex Lab)
arXiv:2606. 11144v1 Announce Type: new Abstract: Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories.
By Abhijoy Sarkar, Aarchi Singh Thakur
GraphBEAT is a temporal graph representation learning framework that bucketizes clinical events from longitudinal electronic health records into time‑ordered graphs, enabling early risk stratification for gastrointestinal cancers. It uses a time‑decay message‑passing mechanism and multi‑head attention pooling to generate patient‑level representations, and was trained on multi‑site diagnosis codes and laboratory tests to predict pancreatic, liver, and intestinal cancer at 12, 24, and 36‑month lead times. Across all experiments, GraphBEAT outperformed baseline graph neural networks and sequence models while requiring far fewer floating‑point operations and parameters.
By Leo Y. Li-Han, Chris Varghese, Elizabeth B. Habermann, Mark J. Truty, William R. G. Perry, Cornelius A. Thiels, Hojjat Salehinejad
arXiv:2610.19178v1 Announce Type: new
Abstract: Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shifts between t...
By Olivier Lefebvre (Department of Computer Science, Universit\'e de Sherbrooke, Sherbrooke, Canada), Hakima Laribi (Department of Computer Science, Universit\'e de Sherbrooke, Sherbrooke, Canada), Dan Poenaru (Department of Pediatric Surgery, Montreal Children's Hospital, Montreal, Canada, Centre for Outcomes Research and Evaluation), Martin Valli\`eres (Medical Physics Unit, Department of Oncology, McGill University, Montreal, Canada)
arXiv:2607. 07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment.
By Muhammet Sami Yavuz, Ayhan Can Erdur, Sabri Mustafa Kahya, Benedikt Wiestler, Jana Lipkova