arXiv:2607. 25609v1 Announce Type: cross Abstract: Understanding disease trajectories from longitudinal clinical data remains challenging due to complex temporal dynamics and heterogeneous patient cohorts.
arXiv:2512. 09185v4 Announce Type: replace-cross Abstract: Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment.
arXiv:2606. 05994v1 Announce Type: new Abstract: Medical knowledge graphs (MKGs) infused with clinical knowledge have been increasingly used to model electronic health records (EHRs) to support interpretable predictions in healthcare domain.
arXiv:2606. 06196v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive brain disorder that gradually affects movement, cognitive function, and behavior.
arXiv:2603. 20452v2 Announce Type: replace Abstract: Longitudinal neuroimaging is essential for modeling disease progression in Alzheimer's disease (AD), yet irregular sampling and missing visits pose substantial challenges for learning reliable temporal representations.
The paper investigates hyperbolic graph representation learning applied to biomedical knowledge graphs for Mendelian-disease differential diagnosis. It shows that hyperbolic embeddings outperform Euclidean baselines on isolated ontology subgraphs while requiring fewer dimensions. In a link-prediction task, hyperbolic models rank candidate diseases for patients, indicating they can leverage hierarchical structure in heterogeneous patient-level graphs.
arXiv:2607. 04912v1 Announce Type: cross Abstract: In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes.
HERMES is a graph-based framework that uses only clinical text to predict patient outcomes. It builds personalized Knowledge Graphs from clinical notes via Large‑Language‑Model‑guided extraction and Contrastive Logic Modeling, capturing temporal dynamics and treatment changes. A Graph Attention Network then synthesizes patient representations, and experiments on MIMIC‑III and MIMIC‑IV show HERMES outperforms text‑only baselines for in‑hospital mortality and 30‑day readmission prediction.
arXiv:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
arXiv:2609.21164v1 Announce Type: new Abstract: Integrating diverse data modalities --- such as clinical notes, laboratory results, and medical imaging --- is essential for advancing clinical decisio...
arXiv:2512. 08029v3 Announce Type: replace Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform.
arXiv:2606. 19140v1 Announce Type: new Abstract: Accurate survival prediction is essential for personalized treatment planning in head and neck cancer, yet remains challenging due to the heterogeneous and high-dimensional nature of multimodal clinical data.
arXiv:2602. 10385v5 Announce Type: replace-cross Abstract: The contemporary paradigm of trajectory learning operates fundamentally at the level of group dynamics, systematically reducing individual-level complexity to fit group-level models, thus rendering effective patient subtyping difficult and individual-level modeling largely out of reach.