Cancer Training in Disparities and Equity (C-TIDE)

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C-TIDE Supplement to Expand Training to AI/ML Approaches in Cancer Control and Cancer Disparities The complexity of how biological, behavioral, psychosocial, cultural, and socioeconomic and other determinants of health contribute to disparities in cancer incidence and influence progression, recurrence, adjustment, and survival remains poorly understood. This limited understanding is in part due to a combination of complex interactions across determinants of these disparities as well as a limited workforce that is well trained in health disparities research. The South Florida Cancer Control Training in Disparities and Equity (South Florida C-TIDE; NCI T32 CA251064), offers an innovative approach and ideally suited research environment to train the next generation of cancer researchers in cancer disparities across the cancer control continuum. The C- TIDE T32 grant (a) implements comprehensive, multidisciplinary, and community-engaged research (CER) research training that addresses multilevel determinants of cancer disparities across traditional and unique underserved communities that experience cancer disparities; (b) provides immersive training opportunities in diverse educational and community environments for enhanced didactic and experiential learning; and (c) increases diversity in the workforce by aiming to recruit trainees who are racial/ethnic minorities, part the LGBTQ community, have disabilities, and other underrepresented groups in the cancer disparities workforce. Given these complex, multilevel interactions that likely influence cancer disparities across several communities, work aimed at reducing such disparities also necessitates the implementation of novel and advanced methodology such as artificial intelligence (AI) and machine learning (ML): two training components that are currently unavailable to C-TIDE trainees. This supplement will fill critical training gaps in our training plan by providing highly relevant advanced methodology in AI/ML that is applicable to research in cancer equity. This methodology will provide skills to our trainees and mentors to apply AI/ML technologies in several research approaches that can, for example, identify risk factors that drive disparities in incidence and early detection; develop risk prediction models by assessing patient data to better understand and develop risk stratification and disease progression trajectories; and evaluate predictors of disparate clinical outcomes across our specific populations based on large sets of community, clinical, treatment and care delivery data. The aims of this supplement are: Aim S1: Train 6 fellows and 4 mentors on AI/ML methodology; and Aim S2: Apply AI/ML methodology to trainee’s: (a) ongoing and/or planned research projects, and (b) grants under preparation for future submissions. Didactics, curricula, and other AI/ML training activities are supported by the Institute for Data Science and Computing (IDSC). Training will involve: a) data processing under the FAIR principle, b) programming with a scripting language, c) cloud computing, and d) AI/ML concepts. The supplement will provide competencies in: (a) Data Science with RStudio; (b) Cloud-based AI/ML; and (c) AI/ML Methods for Data Publishing, Sharing and Grant Applications. Evaluation, Dissemination and Sustainability plans for the supplement are in place.