Big Data Health Science Fellow Program in Infectious Disease Research
Big Data Health Science Fellow Program in Infectious Disease Research
批准号:
10461949
负责人:
Xiaoming Li
金额:
$35.1万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-08-04 至 2026-07-31
关键词:
AIDS/HIV problemAcademic TrainingAddressApplications GrantsAreaArtificial IntelligenceBig DataBig Data to KnowledgeBiometryBusinessesCOVID-19Cessation of lifeClinicalClinical MedicineCommunicable DiseasesCommunitiesCompetenceDataData ScienceData ScientistData SourcesDevelopmentDisciplineDiseaseDisease ProgressionEducation ProjectsEducational CurriculumElectronic Health RecordEntrepreneurshipEnvironmentFaceFacultyFosteringFundingFunding AgencyGenerationsGenomicsGoalsHIVHealthHealth Care ResearchHealth PolicyHealth SciencesHealthcareIndividualIndustryInfectious Diseases ResearchInformation TechnologyInfrastructureKnowledgeMachine LearningManuscriptsMeasuresMedicineMentorsMethodologyMissionNational Institute of Allergy and Infectious DiseaseOutcomePharmacy facilityPublic HealthPublic Health NursingResearchResearch PersonnelResearch Project GrantsResearch ProposalsSchoolsScienceSocial WorkSouth CarolinaStrategic PlanningSystemTalentsTechnologyTimeTrainingTraining ProgramsUnderrepresented PopulationsUnited States National Institutes of HealthUniversitiesVirusWagesWorkforce Developmentbig-data sciencebiomedical data sciencecareercohortcollegecommunity engagementcomorbiditydata acquisitiondata streamsdesigndigitalexperiencefinancial incentivegenomic datahands on researchhealth datahealth science researchhealth traininghigh riskimprovedinnovationinterestmedical schoolsmobile computingnanobiomaterialpeerpeer coachingpreventprofessorprogramspublic health interventionpublic health relevancerecruitskill acquisitionskillssocial mediasynergismtrendwearable device
中文摘要
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
新冠肺炎。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。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Big Data Analytics Emerging Scholar (e-Scholar) Program for Minority Students
-
批准号:10554786
-
项目类别:
-
资助金额:$31.33万
-
财政年份:2023
-
负责人:Xiaoming Li
-
依托单位:
University of South Carolina Big Data Health Science Conference
-
批准号:10751656
-
项目类别:
-
资助金额:$2.0万
-
财政年份:2023
-
负责人:Xiaoming Li
-
依托单位:
Visualizing and predicting new and late HIV diagnosis in South Carolina: A Big Data approach
-
批准号:10815140
-
项目类别:
-
资助金额:$69.91万
-
财政年份:2023
-
负责人:Xiaoming Li
-
依托单位:
Informatics Approach to Identification and Deep Phenotyping of PASC Cases
-
批准号:10574753
-
项目类别:
-
资助金额:$21.79万
-
财政年份:2022
-
负责人:Xiaoming Li
-
依托单位:
Utilizing All of Us data to examine the impact of COVID-19 on mental health among people living with HIV
-
批准号:10657875
-
项目类别:
-
资助金额:$10.73万
-
财政年份:2022
-
负责人:Xiaoming Li
-
依托单位:
Curating a Knowledge Base for Individuals with Coinfection of HIV and SARS-CoV-2: EHR-based Data Mining
-
批准号:10481286
-
项目类别:
-
资助金额:$22.29万
-
财政年份:2022
-
负责人:Xiaoming Li
-
依托单位:
Informatics Approach to Identification and Deep Phenotyping of PASC Cases
-
批准号:10696087
-
项目类别:
-
资助金额:$18.04万
-
财政年份:2022
-
负责人:Xiaoming Li
-
依托单位:
Curating a Knowledge Base for Individuals with Coinfection of HIV and SARS-CoV-2: EHR-based Data Mining
-
批准号:10665078
-
项目类别:
-
资助金额:$18.57万
-
财政年份:2022
-
负责人:Xiaoming Li
-
依托单位:
Big Data Health Science Fellow Program in Infectious Disease Research
-
批准号:10666508
-
项目类别:
-
资助金额:$35.1万
-
财政年份:2021
-
负责人:Xiaoming Li
-
依托单位:
Big Data Health Science Fellow Program in Infectious Disease Research
-
批准号:10311679
-
项目类别:
-
资助金额:$35.1万
-
财政年份:2021
-
负责人:Xiaoming Li
-
依托单位:
Big Data Health Science Fellow Program in Infectious Disease Research
-
批准号:10897421
-
项目类别:
-
资助金额:$10.8万
-
财政年份:2021
-
负责人:Xiaoming Li
-
依托单位:
Multilevel Determinants of Racial and Ethnic Disparities in Maternal Morbidity and Mortality in the Context of COVID-19 Pandemic
-
批准号:10392607
-
项目类别:
-
资助金额:$88.62万
-
财政年份:2021
-
负责人:Xiaoming Li
-
依托单位:
Mitigate the effect of HIV-related stigma through a resilience approach
-
批准号:10401515
-
项目类别:
-
资助金额:$63.03万
-
财政年份:2021
-
负责人:Xiaoming Li
-
依托单位:
BDD CIS: Big Data Driven Clinical Informatics & Surveillance - A Multimodal Database Focused Clinical, Community, & Multi-Omics Surveillance Plan for COVID19
-
批准号:10190370
-
项目类别:
-
资助金额:$62.63万
-
财政年份:2020
-
负责人:Xiaoming Li
-
依托单位:
Big Data analytics of HIV treatment gaps in South Carolina: Identification and prediction
-
批准号:10160773
-
项目类别:
-
资助金额:$58.96万
-
财政年份:2017
-
负责人:Xiaoming Li
-
依托单位:
Big Data analytics of HIV treatment gaps in South Carolina: Identification and prediction
-
批准号:9404773
-
项目类别:
-
资助金额:$65.56万
-
财政年份:2017
-
负责人:Xiaoming Li
-
依托单位:
Theory-based HIV Disclosure Intervention for Parents
-
批准号:9135005
-
项目类别:
-
资助金额:$32.39万
-
财政年份:2015
-
负责人:Xiaoming Li
-
依托单位:
Theory-based HIV Disclosure Intervention for Parents
-
批准号:8517170
-
项目类别:
-
资助金额:$33.88万
-
财政年份:2012
-
负责人:Xiaoming Li
-
依托单位:
Theory-based HIV Disclosure Intervention for Parents
-
批准号:8390381
-
项目类别:
-
资助金额:$37.58万
-
财政年份:2012
-
负责人:Xiaoming Li
-
依托单位:
Theory-based HIV Disclosure Intervention for Parents
-
批准号:8704975
-
项目类别:
-
资助金额:$33.99万
-
财政年份:2012
-
负责人:Xiaoming Li
-
依托单位:
海外基金