Cancer Training in Disparities and Equity (C-TIDE)

C-TIDE补充，以扩大癌症控制和癌症差异中AI/ML方法的培训 生物、行为、社会心理、文化、社会经济和其他决定因素的复杂性 健康状况的差异导致癌症发病率的差异，并影响进展、复发、调整和 人们对生存仍然知之甚少。这种有限的理解部分是由于复杂的 这些差异的决定因素之间的相互作用以及受过良好卫生培训的劳动力有限 差距研究。南佛罗里达癌症控制培训的差异和公平（南佛罗里达 C-TIDE; NCI T32 CA 251064），提供了一种创新的方法和理想的研究环境， 下一代癌症研究人员在癌症控制连续体中的癌症差异。C- TIDE T32赠款（a）实施全面，多学科和社区参与的研究（CER） 研究培训，解决传统和独特的癌症差异的多层次决定因素 （B）提供沉浸式培训机会， 多样化的教育和社区环境，以加强教学和体验式学习;以及（c） 通过招募少数种族/族裔的受训人员，增加劳动力的多样性，部分LGBTQ 社区，残疾人和其他在癌症差距劳动力中代表性不足的群体。鉴于这些 复杂的，多层次的相互作用，可能会影响几个社区的癌症差异，工作旨在 减少这种差异还需要实施新的先进方法 例如人工智能（AI）和机器学习（ML）：目前 C-TIDE受训人员无法获得。此补充将通过提供以下内容，填补我们培训计划中的关键培训空白 AI/ML中高度相关的先进方法，适用于癌症公平研究。这种方法 将为我们的学员和导师提供技能，将AI/ML技术应用于几种研究方法， 例如，可以识别导致发病率和早期发现差异的风险因素; 通过评估患者数据建立模型，以更好地了解和开发风险分层和疾病进展 轨迹;并评估我们特定人群中不同临床结局的预测因素， 大量的社区、临床、治疗和护理提供数据。该补充文件的目的是： 对6名研究员和4名导师进行人工智能/机器学习方法的培训;目标S2：将人工智能/机器学习方法应用于学员的： 正在进行和/或计划进行的研究项目，以及（B）正在为今后提交的研究项目准备赠款。教学法， 课程和其他AI/ML培训活动由数据科学与计算研究所提供支持 （IDSC）。培训将包括：a）根据FAIR原则进行数据处理，B）使用脚本编程 语言，c）云计算，d）AI/ML概念。补编将提供以下方面的能力： 科学与RStudio;（B）基于云的AI/ML;和（c）用于数据发布，共享和 补助金申请。补编的评价、传播和可持续性计划已经到位。

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.