Project Summary/Abstract Head and neck cancer (HNC) patients survive years after oncologic therapy due to increased efficacy of therapy, increased incidences of human papilloma virus related HNC, and decreased numbers of smoking and tobacco related tumors. However, the majority of patients are plagued with long lasting or permanent dry mouth (xerostomia), whose severity, rate of development and resolution after treatment vary largely between survivors. However, current prediction models for dry mouth have intrinsic issues which so far have prevented their practical use in clinical care, including missing or incomplete data, co- occurence of multiple symptoms, variability across populations and across time, and, in the case of HNC and other spatially-dependent cancers, further symptom dependency on the anatomical location of dose within to organs at risk, namely, salivary glands. We propose to develop validated, patient-specific models to interpret radiation dose to salivary glands in order to inform individual treatment and care decisions for patients. Our data science approach circumvents limitations in the state of the art by accounting for more complex dose-reponse models of dry mouth, by calibrating for inter-patient variability, and by predicting symptom development and computing clinical action signals for a new patient based on cohorts of similar patients. The proposed supplement application extends the methodological approach of the parent award by incorporating validation data from an alternative facility, item assessment, and user experience testing of the resultant software model user interface, undertaken within a career development plan designed to enhance and accelerate the capacity of the applicant, who is from a background underrepresented in biomedical sciences, to transition to mentored and independent investigator status, thus enhancing cancer research workforce diversity under this program.