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

Yaohua Wang; Eric A Anyimadu; Clifton David Fuller; Amy Catherine Moreno; Xinhua Zhang; G Elisabeta Marai; Guadalupe Canahuate

doi:10.1109/ichi69079.2026.00054

Journal article

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

Yaohua Wang, Eric A Anyimadu, Clifton David Fuller, Amy Catherine Moreno, Xinhua Zhang, G Elisabeta Marai and Guadalupe Canahuate

Proceedings (IEEE International Conference on Healthcare Informatics. Online), Vol.2026, pp.357-366

06/2026

DOI: 10.1109/ichi69079.2026.00054

PMCID: PMC13470404

PMID: 42597694

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Abstract

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.

Machine Learning

Survival analysis

Long-term toxicity

Symptom burden

Head and neck cancer survivors

Details

Title: Subtitle: Improving Survival Prediction of Head and Neck Cancer Patients by Modeling Long-term Symptom Burden
Creators: Yaohua Wang - University of Iowa
Eric A Anyimadu - University of Iowa
Clifton David Fuller - The University of Texas MD Anderson Cancer Center
Amy Catherine Moreno - The University of Texas MD Anderson Cancer Center
Xinhua Zhang - University of Illinois Chicago
G Elisabeta Marai - University of Illinois Chicago
Guadalupe Canahuate - University of Iowa
Resource Type: Journal article
Publication Details: Proceedings (IEEE International Conference on Healthcare Informatics. Online), Vol.2026, pp.357-366
DOI: 10.1109/ichi69079.2026.00054
PMID: 42597694
PMCID: PMC13470404
NLM abbreviation: Proc (IEEE Int Conf Healthc Inform)
ISSN: 2575-2626
eISSN: 2575-2634
Publisher: IEEE
Grant note: R01 CA258827 / NCI NIH HHS
Language: English
Date published: 06/2026
Academic Unit: Electrical and Computer Engineering; Internal Medicine
Record Identifier: 9985218626002771

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