Improving Survival Prediction of Head and Neck Cancer Patients by Modeling Long-term Symptom Burden

Accurate survival prediction in head and neck cancer (HNC) is essential for effective treatment planning and management. Patient-reported outcomes (PROs) provide a complementary source of prognostic information by capturing longitudinal symptom trajectories that reflect patients' health status and treatment response, but their integration into survival modeling remains challenging due to missing data and limited availability at the desired decision points. We propose an end-to-end approach that models longitudinal symptom trajectories using a bidirectional long short-term memory (Bi-LSTM) network to forecast late symptom burden from baseline PROs. Predicted symptom trajectories are summarized into cluster-level average symptom burden scores, which are standardized and incorporated as continuous covariates into a Cox proportional hazards model alongside clinical features. The proposed approach improves test-set prognostic discrimination, increasing the concordance index from 0.802 to 0.842. These results demonstrate that forecasting longitudinal PRO dynamics and representing symptom burden as continuous, data-driven measures can enhance personalized survival prediction in HNC.