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.

Index Terms: Patient reported outcomes (PRO), deep learning, patient clustering, regression, survival analysis, xerostomia.

https://doi.org/10.1109/JBHI.2024.3515092

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Anyimadu, E. A., Wang, Y., Floricel, C., Kamel, S., Fuller, C. D., Zhang, X., Marai, G. E., Canahuate, G. M., PRO-Based Stratification Improves Model Prediction for Toxicity and Survival of Head and Neck Cancer Patients, IEEE Journal of Biomedical and Health Informatics, vol 29, no 2, pp. 807-814, February 25th, 2025. https://mdanderson.elsevierpure.com/en/publications/pro-based-stratification-improves-model-prediction-for-toxicity-a