Longitudinal deep learning models for integrating patient-reported outcomes in toxicity and survival prediction of head and neck cancer
Year: 2025
Type: dissertation
Abstract: In clinical practice, head and neck cancer (HNC) patients usually have severe side effects during the cancer treatment that subsides over time. However, some patients have persistent late toxicity that negatively affects the patient's quality of life (QoL) after the treatment. To monitor patients' symptom burden, clinicians often collect symptom ratings directly from patients using symptom questionnaires known as patient-reported outcome (PRO) measures. These PRO can be collected repeatedly over time and could be used to better inform treatment decisions. However, their incorporation into clinical practice presents numerous challenges. First, missing data are common if patients skip certain questions or entire questionnaires. Second, PROs are subjective: a symptom rating of 5 for one patient could be a 10 for another. Finally, only a subset of patients experience late toxicity, while most see their symptoms diminish over time.
DOI: 10.25820/etd.007868
Author: Wang, Yaohua
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6927031276
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