Cancer Prevention and Control (CAPAC) Research Training Program

癌症预防与控制 (CAPAC) 研究培训计划

• 批准号: 10405752
• 负责人: ANA Patricia ORTIZ
• 金额: $ 8.39万
• 依托单位: COMPREHENSIVE CANCER CENTER/ UNIV/PR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405752
• 关键词: Affect; Algorithms; Artificial Intelligence; Cancer Control; Cancer Control Research; Cause of Death; Classification; Competence; Comprehensive Cancer Center; Data; Data Science; Data Set; Ethnic Origin; Evaluation; FAIR principles; Grant; Health; Health Occupations; Hispanics; Hour; Human Resources Development; Institution; Machine Learning; Malignant Neoplasms; Mentors; Methods; Minority Groups; Modeling; Parents; Participant; Phase; Predictive Cancer Model; Process; Puerto Rico; Pythons; Research; Research Personnel; Students; Supervision; Techniques; Testing; Training; Training Programs; United States; Universities; base; cancer diagnosis; cancer health disparity; cancer prevention; cancer therapy; conditioning; data wrangling; experience; interest; model building; online course; parent grant; racial and ethnic; recruit; skills; summer research; tool

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.

概括 癌症是西班牙裔美国人的首要死因，西班牙裔美国人是美国最大的种族/族裔群体 各州受癌症健康差异的影响尤为严重。尽管存在这种差异，癌症数据集， 特别是针对西班牙裔人口，不像其他种族那样可用。鉴于癌症的需要 健康差异研究重点关注西班牙裔健康，需要应用人工 该领域的智能/机器学习 (AI/ML) 方法以及使西班牙裔患癌症的紧迫性 数据集可查找、可访问、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与控制研究（CAPAC）培训项目 中心（UPRCCC），招募研究生和卫生专业学生进行暑期实践研究 公关经验。本补充材料旨在扩大家长 CAPAC 培训计划的范围并为 1）操纵和预处理西班牙裔癌症的技术和方法的研究人员 数据集，使它们为 FAIR 和 AI/ML 做好准备，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，重点关注西班牙裔 数据集。我们将开发基于数据科学项目生命周期的在线课程，其中包括四个 阶段：1）数据理解/数据预处理； 2）数据整理； 3）车型规划； 4) 型号 建筑。这个 24 小时在线异步课程将分为两个部分的模块。 第 1 部分将包括以下主题：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；编程与 Python;探索、预处理和调整癌症数据集；执行提取、转换、加载 (ETL) 在 AI/ML 建模之前。第 2 部分将添加以下主题：AI/ML 原理；变量相关性 和协会；确定训练和测试的数据集；有监督和无监督的机器学习方法； 分类、回归和集成 ML 算法；熟悉机器学习工具。为了开发我们的课程， 示例和项目，我们将使用来自美国和波多黎各的西班牙裔癌症数据集。该课程将 感兴趣的参与者自愿且免费（可容纳 40 名学员），包括 CAPAC 参与者（校友） 申请人、CAPAC 导师以及来自合作资助机构/机构的实习生和研究人员。 学生获得的技能将通过测验和最终实践项目进行评估，而课程将 在母公司补助金评估部分的支持下进行评估。此补充将影响 来自美国和波多黎各的人力资源开发（例如学生、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 创建基于机器学习的癌症预测模型的方法。