The paper presents a visual analytics framework that models and interprets time‑varying care team interactions in oncology. It builds dynamic collaboration networks from electronic health record communication data and applies a temporal graph neural network to estimate patient risk over time. The system integrates risk modeling with coordinated visualizations, feature‑level explanations, and what‑if analysis to help analysts explore how changes in team composition relate to risk shifts across treatment stages.
Cancer care is a longitudinal, time-sensitive process requiring coordination among diverse healthcare professionals (HCPs), whose collaboration evolves throughout treatment. Prior studies link care te...
arXiv:2606. 04499v1 Announce Type: cross Abstract: Cancer care requires a longitudinal approach in which treatments are planned and delivered over time according to the needs of each individual patient.
arXiv:2512. 03296v2 Announce Type: replace-cross Abstract: Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams.
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.
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.
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. 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: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:2605. 07267v2 Announce Type: replace Abstract: Personalized healthcare decisions require reasoning about how physiological and behavioral variables influence an individual patient over time.
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...
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:2608. 14657v1 Announce Type: new Abstract: Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single data modalities and their inability to leverage structured clinical knowledge.