Informatics Approach to Identification and Deep Phenotyping of PASC Cases

PASC 病例识别和深度表型分析的信息学方法

基本信息

  • 批准号:
    10574753
  • 负责人:
  • 金额:
    $ 21.79万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-06 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

PROJECT SUMMARY/ABSTRACT Increasingly there have been reports of persistent symptoms and multi-organ multi-system manifestations (e.g., pulmonary, cardiovascular, renal, and neurological) among individuals who were recovered from the acute phase of COVID-19, denoted as Post-Acute Sequela of SARS-CoV-2 infection (PASC). Given that 76.7 million people are known to have been infected in the US as of February of 2022, millions of people will potentially experience PASC. This projected disease burden will have a profound public health impact with respect to patients' clinical outcomes and US health systems during post-COVID-19 care. Timely identification of individuals with PASC from existing COVID-19 cohorts and newly identified COVID-19 patients is urgently needed for PASC clinics and longitudinal cohort studies on PASC. Building on biomedical informatics methodologies, we propose a high- throughput and semi-supervised Deep Phenotyping approach to identifying individuals with PASC and characterizing their phenotypes. Our approach is based on a Graph representational model constructed based on the South Carolina COVID-19 Cohort (S3C), funded by the National Institute of Allergy and Infectious Diseases (NIAID) (R01A127203-4S1). S3C (n=~1,400, 000 COVID-19 patients by the February of 2022) is a multi-modal data repository consisting of EHR, health systems data, community-based health services data, and claims data, with complete temporal trajectory of every datum at individual-level. Building on top of the Graph model, we will detect phenotypes of candidate PASC patients by using unsupervised clustering algorithms. We will then identify and validate clinically plausible PASC cases and corresponding phenotypes by incorporating clinical evaluation and supervised algorithms. This study will result in a high-throughput algorithm application for identifying and characterizing PASC cases from COVID-19 EHR cohorts. The resulted EHR and machine learning models are interpretable, generalizable, and will form a foundation for testing and implementing in state-wide and national post-COVID clinics/programs.
项目摘要/摘要 越来越多地有关于持续性症状和多器官多系统表现的报道(例如, 肺、心血管、肾脏和神经系统)从急性期恢复的个体 新冠肺炎的后遗症,称为SARS-CoV-2感染的急性后遗症。鉴于7670万人 截至2022年2月在美国已知已被感染,数百万人可能会经历 PASC。这种预计的疾病负担将对患者的临床健康产生深远的影响 后新冠肺炎护理期间的结果和美国卫生系统。及时识别患有PASC的个人 现有的新冠肺炎队列和新发现的新冠肺炎患者迫切需要用于PASC诊所和 PASC的纵向队列研究。在生物医学信息学方法学的基础上,我们提出了一个高度- 吞吐量和半监督深度表型方法识别PASC和PASC患者 来描述它们的表型。我们的方法是基于基于 关于南卡罗来纳州新冠肺炎队列(S3C),由国家过敏和传染病研究所资助 疾病(NIAID)(R01A127203-4S1)。S3C(截至2022年2月,约1,400,000名新冠肺炎患者)是 多模式数据存储库,包括电子病历、卫生系统数据、基于社区的卫生服务数据和 索赔数据,每个数据在个人层面上的完整时间轨迹。建立在图形的顶部 模型中,我们将使用非监督聚类算法检测候选PASC患者的表型。我们 然后,通过将PASC病例和相应的表型 临床评估和监督算法。这项研究将导致高通量算法的应用 用于识别和描述新冠肺炎电子病历队列中的PASC病例。由此产生的EHR和机器 学习模型是可解释的、可概括的,并将形成测试和实现的基础 全州和全国的COVID后诊所/计划。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Xiaoming Li其他文献

Xiaoming Li的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Xiaoming Li', 18)}}的其他基金

Big Data Analytics Emerging Scholar (e-Scholar) Program for Minority Students
少数民族学生大数据分析新兴学者(e-Scholar)计划
  • 批准号:
    10554786
  • 财政年份:
    2023
  • 资助金额:
    $ 21.79万
  • 项目类别:
University of South Carolina Big Data Health Science Conference
南卡罗来纳大学大数据健康科学会议
  • 批准号:
    10751656
  • 财政年份:
    2023
  • 资助金额:
    $ 21.79万
  • 项目类别:
Visualizing and predicting new and late HIV diagnosis in South Carolina: A Big Data approach
可视化和预测南卡罗来纳州新的和晚期的艾滋病毒诊断:大数据方法
  • 批准号:
    10815140
  • 财政年份:
    2023
  • 资助金额:
    $ 21.79万
  • 项目类别:
Utilizing All of Us data to examine the impact of COVID-19 on mental health among people living with HIV
利用 All of Us 数据研究 COVID-19 对 HIV 感染者心理健康的影响
  • 批准号:
    10657875
  • 财政年份:
    2022
  • 资助金额:
    $ 21.79万
  • 项目类别:
Curating a Knowledge Base for Individuals with Coinfection of HIV and SARS-CoV-2: EHR-based Data Mining
为 HIV 和 SARS-CoV-2 混合感染者打造知识库:基于 EHR 的数据挖掘
  • 批准号:
    10481286
  • 财政年份:
    2022
  • 资助金额:
    $ 21.79万
  • 项目类别:
Informatics Approach to Identification and Deep Phenotyping of PASC Cases
PASC 病例识别和深度表型分析的信息学方法
  • 批准号:
    10696087
  • 财政年份:
    2022
  • 资助金额:
    $ 21.79万
  • 项目类别:
Curating a Knowledge Base for Individuals with Coinfection of HIV and SARS-CoV-2: EHR-based Data Mining
为 HIV 和 SARS-CoV-2 混合感染者打造知识库:基于 EHR 的数据挖掘
  • 批准号:
    10665078
  • 财政年份:
    2022
  • 资助金额:
    $ 21.79万
  • 项目类别:
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:
    10666508
  • 财政年份:
    2021
  • 资助金额:
    $ 21.79万
  • 项目类别:
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:
    10311679
  • 财政年份:
    2021
  • 资助金额:
    $ 21.79万
  • 项目类别:
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
  • 财政年份:
    2021
  • 资助金额:
    $ 21.79万
  • 项目类别:

相似海外基金

Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
  • 批准号:
    MR/S03398X/2
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Fellowship
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
  • 批准号:
    EP/Y001486/1
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Research Grant
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
  • 批准号:
    2338423
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Continuing Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
  • 批准号:
    MR/X03657X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
  • 批准号:
    2348066
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
  • 批准号:
    AH/Z505481/1
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10107647
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    EU-Funded
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
  • 批准号:
    2341402
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Standard Grant
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
  • 批准号:
    10106221
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
  • 批准号:
    AH/Z505341/1
  • 财政年份:
    2024
  • 资助金额:
    $ 21.79万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了