Publikation
VISTA: Velocity-Informed Sequential Transition Analysis
Prajvi Saxena; Lina Charlotte Jeran; Sabine Janzen; Iris Blotenberg; Jochen Rene ́ Thyrian; Wolfgang Maass
In: 2026 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN-2026), located at IJCNN-2026, June 22-26, Maastricht, Netherlands, IEEE World Congress on Computational Intelligence, 2026.
Zusammenfassung
Accurate prediction of dementia progression is crucial for timely intervention and care planning, yet remains challenging due to highly variable patient trajectories. Recent machine learning approaches rely on neuroimaging and genetic biomarkers. While effective, these methods are costly, invasive, and difficult to deploy at scale in primary care settings. In contrast, routinely collected longitudinal clinical data, such as questionnaire based assessments and care intervention records remain underutilized for prognosis. We propose VISTA (Velocity- Informed Sequential Transition Analysis), a framework for dementia prognosis using real-world clinical data. It explicitly encodes temporal velocity - the rate of change in clinical features between consecutive assessments together with the absolute clinical state. It addresses the limitation of existing methods that treat assessments as independent snapshots. Our framework converts longitudinal prediction as transition-level forecasting via sliding-window decomposition, which increases the number of training instances while preserving patient-specific temporal structure. We implement it using gradient boosted decision trees to capture non-linear state-velocity interactions while handling missing data natively. We evaluate the framework on DelpHi dataset an 8-year real-world primary care longitudinal dataset comprising of 459 patients (768 annual transitions). VISTA achieves R^2 = 0.74 and MAE = 2.63 MMSE points, substantially outperforming linear mixed models (R^2 = 0.25) and standard baselines in cognitive prognosis. Ablation studies and SHAP analysis also revealed several crucial patterns. Qualitative analysis shows VISTA accurately captures systematic decline patterns while still maintaining predictions on volatile trajectories. These results demonstrate that velocity-aware modeling enables accurate dementia prognosis.