Big Data Health Science Fellow Program in Infectious Disease Research

传染病研究大数据健康科学研究生计划

• 批准号: 10897421
• 负责人: Xiaoming Li
• 金额: $ 10.8万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-04 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10897421
• 关键词: Academic Training; Address; Applications Grants; Area; Artificial Intelligence; Big Data; Big Data to Knowledge; Biometry; Businesses; COVID-19; Cessation of life; Clinical; Clinical Medicine; Communicable Diseases; Communities; Competence; Data; Data Science; Data Scientist; Data Sources; Development; Discipline; Discipline of Nursing; Disease; Disease Progression; Education Projects; Educational Curriculum; Electronic Health Record; Entrepreneurship; Environment; Face; Faculty; Fostering; Funding; Funding Agency; Generations; Genomics; Goals; HIV; HIV/AIDS; Health; Health Care Research; Health Policy; Health Sciences; Healthcare; Individual; Industry; Infectious Diseases Research; Information Technology; Infrastructure; Knowledge; Machine Learning; Manuscripts; Measures; Medicine; Mentors; Methodology; Mission; National Institute of Allergy and Infectious Disease; Outcome; Pharmacy facility; Public Health; Research; Research Personnel; Research Project Grants; Research Proposals; Schools; Science; Social Work; South Carolina; Strategic Planning; System; Talents; Technology; Time; Training; Training Programs; Underrepresented Populations; United States National Institutes of Health; Universities; Virus; Wages; Workforce Development; big-data science; biomedical data science; career; cohort; college; community engagement; comorbidity; data acquisition; data streams; design; digital; experience; financial incentive; genomic data; hands on research; health data; health science research; health training; high risk population; improved; innovation; interest; mobile computing; nanomaterials; peer; peer coaching; prevent; professor; programs; public health intervention; public health relevance; recruit; skill acquisition; skills; social media; synergism; trend; wearable device

Abstract The multiple, massive, and rich Big Data streams in healthcare (e.g., electronic health records, mobile technologies, wearable devices, genomic data) and the emergence of advanced information and computational technologies (e.g., machine learning and artificial intelligence) offer an invaluable opportunity for applying innovative Big Data science research in NIAID focus areas of infectious diseases such as HIV/AIDS and COVID-19. Big Data science has the potential to identify high-risk individuals and communities and prioritize them for early biomedical or public health interventions, predict long-term clinical outcomes and disease progression, and evaluate public health policy impact. Key to addressing these complexities is a critical mass of health researchers with adequate knowledge, competencies, and skills to unlock important answers from Big Data to better understand, treat, and ultimately prevent these diseases and related comorbidities. However, there is a nationwide shortage of talent with such knowledge, competencies, and skills, especially in traditional academic settings. While junior faculty, as part of the generations of digital learners, have the greatest potential to develop their Big Data health science research agenda, many face multiple structural barriers to conduct Big Data science research. Such barriers include a lack of protected time to initiate new interdisciplinary Big Data research, lack of opportunity to participate in funded Big Data research, and a lack of adequate mentoring. To address these gaps, we propose developing a “Big Data Heath Science Fellow” program for early career junior faculty (i.e., assistant professors) at health science schools (e.g., medicine, public health, nursing, pharmacy, social work) at the University of South Carolina (USC). Specifically, we plan to recruit 4 USC health science junior faculty per year and provide them with protected time (25%) to participate in the comprehensive training program, including: 1) courses for competency and skill development in Big Data research and professional development; 2) participation in hands-on research and grant proposal development; and 3) rich mentoring experience in Big Data research and professional development. The proposed training program will be implemented with the support of the existing infrastructure of the USC Big Data Health Science Center (BDHSC), one of USC’s Excellence Initiatives. BDHSC’s mission is to promote and support Big Data health science research at USC and across SC through capacity development, academic training, professional development, community engagement, and methodological advancement. BDHSC contains 5 content cores (electronic health records, geospatial, genomic, social media, and bio-nanomaterial data) and 2 supporting hubs (business/entrepreneurship and technology) with the involvement of 43 faculty from 10 USC college/schools. The proposed training will be an integral component of the BDHSC professional development mission. Upon the accomplishment of the proposed training, each trainee will be expected to: 1) obtain hands- on mentored research experience on an NIAID-funded project; 2) develop at least one Big Data-related manuscript on HIV or COVID-19; and 3) submit one grant application to NIAID or other appropriate funding source. The training program will foster a research environment to encourage individuals from diverse backgrounds, including those from underrepresented groups, to pursue further Big Data health science research in HIV, COVID-19, and other NIAID focus areas.

摘要 医疗保健中的多个、大规模和丰富的大数据流（例如，电子健康记录，移动的 技术、可穿戴设备、基因组数据）以及先进信息和计算技术的出现， 技术（例如，机器学习和人工智能）提供了一个宝贵的机会， NIAID重点传染病领域的创新大数据科学研究，如艾滋病毒/艾滋病和 2019冠状病毒病。大数据科学有可能识别高风险的个人和社区， 它们用于早期生物医学或公共卫生干预，预测长期临床结果和疾病 评估公共卫生政策的影响。解决这些复杂性的关键是要有一个临界质量 健康研究人员拥有足够的知识，能力和技能，可以从大 数据，以更好地了解，治疗，并最终预防这些疾病和相关的合并症。然而，在这方面， 具有这种知识、能力和技能的人才在全国范围内都很短缺，特别是在传统行业。 学术环境。而初级教师作为数字学习者的一部分， 为了发展他们的大数据健康科学研究议程，许多人面临着进行大数据研究的多重结构性障碍。 数据科学研究。这些障碍包括缺乏受保护的时间来启动新的跨学科大数据 研究，缺乏参与资助的大数据研究的机会，以及缺乏足够的指导。到 为了解决这些差距，我们建议为职业生涯初期的年轻人开发一个“大数据健康科学研究员”计划， 教员（即，助理教授）在健康科学学校（例如，医学、公共卫生、护理、药学、 社会工作）在南卡罗来纳州大学（USC）。具体来说，我们计划招募4名南加州大学健康科学 并为他们提供受保护的时间（25%）参加综合培训 计划，包括：1）大数据研究和专业能力和技能发展课程 开发; 2）参与实践研究和赠款提案开发;以及3）丰富的指导 大数据研究和专业发展经验。拟议的培训方案将是 在南加州大学大数据健康科学中心现有基础设施的支持下实施 （BDHSC），南加州大学的卓越计划之一。BDHSC的使命是促进和支持大数据健康 科学研究在南加州大学和整个SC通过能力发展，学术培训，专业 发展、社区参与和方法进步。BDHSC包含5个内容核心 （电子健康记录、地理空间、基因组、社交媒体和生物纳米材料数据）和2个支持 中心（商业/创业和技术），来自10个南加州大学的43名教师参与 学院/学校。拟议的培训将是BDHSC专业发展的一个组成部分 使命。在完成拟议的培训后，每个受训者将被期望：1）获得手- 在NIAID资助的项目上的指导研究经验; 2）开发至少一个与大数据相关的 关于艾滋病毒或COVID-19的手稿;以及3）向NIAID或其他适当的资金提交一份赠款申请 源头培训计划将培养一个研究环境，鼓励来自不同领域的个人 背景，包括那些来自代表性不足的群体，进一步追求大数据健康科学 在艾滋病毒，COVID-19和其他NIAID重点领域的研究。

项目成果

A Bayesian Spatial Scan Statistic for Multinomial Data.

多项数据的贝叶斯空间扫描统计。

• DOI: 10.1016/j.spl.2023.110005
• 发表时间: 2024
• 期刊: Statistics & probability letters
• 影响因子: 0.8
• 作者: Self,Stella;Nolan,Melissa
• 通讯作者: Nolan,Melissa

Ecologic analysis of antimicrobial use in South Carolina hospitals during 2020-2022.

• DOI: 10.1017/ash.2023.496
• 发表时间: 2023
• 期刊: Antimicrobial stewardship & healthcare epidemiology : ASHE
• 作者: Bailey, Pamela;Chen, Shujie;Al-Hasan, Majdi N;Olatosi, Bankole;Li, Xiaoming;Zhang, Jiajia
• 通讯作者: Zhang, Jiajia