ArticleIEEE journal of biomedical and health informatics2025
PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients.
Article in IEEE journal of biomedical and health informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Improving Survival Prediction of Head and Neck Cancer Patients by Modeling Long-term Symptom Burden.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Advancing Normal Tissue Complication Probability Modeling with Supervised Contrastive Learning for Predicting Osteoradionecrosis.Proceedings of the ... Symposium on Applied Computing. Symposium on Applied Computing · 2026Article
- Lessons from the Development and Deployment of an Interactive Oncological Risk Estimator.... IEEE Workshop on Visual Analytics in Healthcare. IEEE Workshop on Visual Analytics in Healthcare · 2025Article
- Dual-Attention BiLSTM for Interpretable Forecasting of Treatment Toxicities.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2025Article
- Evaluating Dimensionality Reduction for Patient-Reported Outcome-Based Survival Modeling in Patients With Head and Neck Cancer.JCO clinical cancer informatics · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Patient-Reported Outcomes (PRO) consist of information provided directly by the patients about their health status including symptom ratings. PROs are commonly used in clinical practice to support clinical decision-making and have recently been incorporated into machine learning models to improve risk prediction. In this work, we aim to evaluate whether the inclusion of a patient stratification based on 12-month post-treatment predicted Patient Reported Outcomes improves risk prediction of radiation-induced toxicity and overall survival for head and neck cancer patients. A bidirectional long-short term memory (Bi-LSTM) recurrent neural network was used to model the longitudinal PRO data and to predict symptom ratings 12 months post-treatment. Patients were stratified using hierarchical clustering over the LSTM-predicted data. A logistic regression model was trained to predict Xerostomia at 12 months and a Cox regression model to predict overall survival. Results show that the inclusion of symptom burden clusters derived from the predicted Patient Reported Outcomes improves radiation-induced toxicity and overall survival prediction for head and neck cancer patients.
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Registered trials
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