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. While deep survival models have improved predictive performance over classical statistical approaches, existing methods typically rely on static fusion strategies or temporally agnostic modeling, limiting their ability to capture structured clinical workflows. In this work, we propose ChronoSurv, a heterogeneous hierarchical directed graph f
The proliferation of multimodal clinical data and advancements in graph neural networks are enabling more sophisticated approaches to patient-specific survival prediction.
Improved predictive models for cancer survival directly inform personalized treatment strategies, potentially enhancing patient outcomes and healthcare efficiency.
This work introduces a more dynamic and clinically pathway-guided approach to survival analysis, moving beyond static fusion methods to better represent temporal clinical workflows.
- · Oncology patients
- · Healthcare providers
- · AI in medicine developers
- · Pharmaceutical companies
- · Traditional statistical survival models
More accurate and personalized treatment plans for head and neck cancer patients become possible.
The framework could be adapted for other complex diseases, leading to broader applications of clinical pathway-guided AI in healthcare.
This progression may accelerate the integration of AI-driven decision support systems into standard clinical practice, transforming medical diagnostics and prognostics.
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Read at arXiv cs.LG