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 癌症是曼联最大的种族/族裔少数民族的西班牙裔死亡的主要原因 国家和受癌症健康差异的影响不成比例。尽管存在这种差异，癌症数据集， 鉴于需要癌症 健康差异研究以西班牙裔健康为重点，需要应用人工 智能/机器学习（AI/ML）在该领域采用，并迫切需要使西班牙裔癌症 数据集可发现，可访问，可互操作和可重复使用（fair）和AI/ML -Ready。预防癌症 波多黎各大学综合癌症的控制研究（CAPAC）培训计划 中心（UPRCCC），招募毕业生和卫生专业人员的学生进行夏季研究 PR的经验。这种补充旨在扩大父母CAPAC培训计划的范围并准备 研究工作人员1）操纵和预处理西班牙裔癌症的技术和方法 数据集使它们公平且AI/ML准备就绪，并且在2）开发基于ML的模型的可用方法 分析这些数据并为癌症诊断和治疗创建预测模型，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）数据争吵； 3）模型计划； 4）模型 建筑。这个24小时的在线异步课程将在两个组件的模块中组织。 组件1将包括以下主题：癌症数据类型的基本原理，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，预处理和调理癌症数据集；执行提取，转换，负载 （ETL）在AI/ML建模之前。组件2将添加主题，例如：AI/ML原理；可变相关性 和协会；确定用于培训和测试的数据集；监督和无监督的ML方法； 分类，回归和合奏ML-Algorithms；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。课程会 对于有趣的参与者（包括40名学员的能力），包括CAPAC参与者（校友）自愿和免费 以及申请人，Capac导师，以及合作赠款/机构的学员和研究人员。 学生的获得技能将通过测验和最终实用项目进行评估，而课程将是 在父母赠款的评估部分的支持下进行评估。这种补充将影响 美国和波多黎各的人力资源（例如学生，研究人员，临床医生）的发展 具有使公平的西班牙裔癌症数据集并应用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。