Cancer Prevention and Control (CAPAC) Research Training Program

总结 癌症是西班牙裔美国人死亡的主要原因，西班牙裔美国人是美国最大的种族/少数民族群体。 国家和不成比例地受到癌症的健康差距。尽管存在这种差异，癌症数据集， 特别是西班牙裔人口，不像其他种族那样可用。考虑到癌症的需要 健康差距研究，重点是西班牙裔健康，有必要应用人工 智能/机器学习（AI/ML）在这一领域的方法，以及使西班牙裔癌症的紧迫性 数据集可查找，可扩展，可互操作和可重用（FAIR）和AI/ML就绪。癌症预防 波多黎各大学综合癌症研究与控制（卡帕克）培训计划 中心（UPRCCC），招募研究生和卫生专业的学生进行暑期实践研究 本补充材料旨在扩大母公司卡帕克培训计划的范围， 研究人员1）操纵和预处理西班牙裔癌症的技术和方法 数据集，使其FAIR和AI/ML准备就绪，以及2）开发基于ML的模型的可用方法 分析这些数据，并创建癌症诊断和治疗的预测模型，重点是西班牙裔 数据集。我们将根据数据科学项目生命周期开发一门在线课程，其中包括四个 阶段：1）数据理解/数据预处理; 2）数据管理; 3）模型规划; 4）模型 建设这个24小时的在线异步课程将分为两个部分。 组件1将包括以下主题：癌症数据类型的基础知识，识别和理解 癌症数据集、数据科学概念和项目生命周期;基本编程概念;编程 Python;探索、预处理和调节癌症数据集;执行提取、转换和加载 (ETL)在AI/ML建模之前。组件2将添加主题，例如：AI/ML的原则;变量相关性 和关联;确定用于训练和测试的数据集;监督和无监督ML方法; 分类，回归和集成ML算法;熟悉ML工具。为了发展我们的课程， 例子和项目，我们将使用来自美国和PR的西班牙裔癌症数据集。 自愿和免费供感兴趣的参与者参加（可容纳40名受训人员），包括卡帕克参与者（校友） 和申请人，卡帕克导师，以及学员和研究人员从合作赠款/机构。 学生获得的技能将通过测验和最后的实践项目进行评估，而课程将 在父母补助金评价部分的支持下进行评价。这一补充将影响 开发美国和波多黎各的人力资源（如学生、研究人员、临床医生） 具备制作FAIR西班牙裔癌症数据集和应用AI/ML所需的能力和技能 创建基于ML的癌症预测模型的方法。

Summary Cancer is the leading cause of death among Hispanics, the largest racial/ethnic minority group in the United States and disproportionately affected by cancer health disparities. Despite this disparity, cancer datasets, specifically for Hispanic populations, are not as available as for other ethnicities. Given the need for cancer health disparities research with a focus on Hispanic health, there is a need for applying Artificial Intelligence/Machine Learning (AI/ML) approaches in this field, and an urgency on making Hispanics cancer datasets Findable, Accessible, Interoperable, and Reusable (FAIR) and AI/ML -ready. The Cancer Prevention and Control Research (CAPAC) Training Program of the University of Puerto Rico Comprehensive Cancer Center (UPRCCC), recruits graduate and health professions students for a hands-on summer research experience in PR. This supplement aims to expand the scope of the parent CAPAC training Program and prepare research workforce on 1) the techniques and approaches to manipulate and pre-process Hispanics cancer datasets to make them FAIR and AI/ML ready, and on 2) the available methods for developing ML-based models to analyze these data and create predictive models for cancer diagnosis and treatments with a focus on Hispanic datasets. We will develop an online course based on the data science project lifecycle, which includes four phases: 1) Data Understanding/ Data Pre-processing; 2) Data Wrangling; 3) Model Planning; and 4) Model Building. This 24-hour online asynchronous course will be organized in modules within two components. Component 1 will include the following topics: fundamentals of cancer data types, identifying and understanding cancer datasets, data science concepts and project lifecycles; basic programming concepts; programming with Python; exploring, pre-processing, and conditioning the cancer datasets; performing Extract, Transform, Load (ETL) prior to AI/ML modelling. Component 2 will add topics such as: principles of AI/ML; variable correlations and associations; determining datasets for training and testing; supervised and unsupervised ML approaches; classification, regression and ensembles ML-algorithms; familiarizing with ML tools. To develop our course, examples and projects, we will use Hispanics cancer datasets from the US and PR. The course would be voluntary and free for interested participants (capacity of 40 trainees), including CAPAC participants (alumni) and applicants, CAPAC mentors, as well as trainees and research staff from collaborating grants/institutions. Student’s gained skills will be evaluated with quizzes and a final practical project, while the course will be evaluated with the support of the evaluation component of the parent grant. This supplement will impact the development of human resources (e.g. students, researchers, clinicians) from the United States and Puerto Rico with the competencies and skills needed to make FAIR Hispanics cancer datasets and to apply AI/ML approaches for creating ML-based predictive models for cancer.