AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids

AICORE-kids:针对儿童的人工智能 COVID-19 风险评估

基本信息

  • 批准号:
    10272787
  • 负责人:
  • 金额:
    $ 81.75万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-01-01 至 2022-04-30
  • 项目状态:
    已结题

项目摘要

This work is directed at characterizing pediatric COVID-19 and stratifying incoming patients by projected (future) disease severity. Such stratification has several implications: immediately improving treatment planning, and as disease mechanistic pathways are uncovered, directing treatment. Predicting future severity will inform the risks of outpatient treatment; to the patients themselves, their family, other caregivers/cohabitants, and to schools and employers. As varying levels of “reopening” are adopted across the country (and the world), such prognostication will inform policy on the handling of pediatric carriers in the community. Based on our preliminary analysis we assert that a combination of novel assays including quantitative serology inflammatory markers (cytokine/chemokine profiles, immune profiles), transcriptomics, epigenomics, longitudinal physiological monitoring, time series analysis, imaging, radiomics and clinical observation including social determinants of health, contains adequate information even at early stages of infection to stratify the disease and predict disease severity. We propose an artificial intelligence/machine learning approach to integrate this rich and heterogeneous dataset, characterize the spectrum of disease and identify biosignatures that predict severity in progressive disease. To facilitate translation of the approaches developed in this work to a wide user community, we incorporate a Translational Development function, to oversee the design-control process and ensure readiness of our methods for regulatory review. Incorporated into our timelines are appropriate regulatory milestones intended to conform with the Emergency Use Authorization (EUA) programs in effect for SARS- CoV-2 diagnostics.
这项工作旨在确定儿童COVID-19的特征,并通过预测对入院患者进行分层

项目成果

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CARL E ALLEN其他文献

CARL E ALLEN的其他文献

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{{ truncateString('CARL E ALLEN', 18)}}的其他基金

AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids
AICORE-kids:针对儿童的人工智能 COVID-19 风险评估
  • 批准号:
    10320488
  • 财政年份:
    2021
  • 资助金额:
    $ 81.75万
  • 项目类别:
AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids
AICORE-kids:针对儿童的人工智能 COVID-19 风险评估
  • 批准号:
    10733689
  • 财政年份:
    2021
  • 资助金额:
    $ 81.75万
  • 项目类别:
AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids
AICORE-kids:针对儿童的人工智能 COVID-19 风险评估
  • 批准号:
    10847803
  • 财政年份:
    2021
  • 资助金额:
    $ 81.75万
  • 项目类别:
The PreVAIL-kIds Common Protocol
PreVAIL-kIds 通用协议
  • 批准号:
    10872420
  • 财政年份:
    2021
  • 资助金额:
    $ 81.75万
  • 项目类别:
Establishing a Platform for Clinical Improvement for Children with HIV-Associated Malignancies in Sub-Saharan Africa
为撒哈拉以南非洲地区患有艾滋病毒相关恶性肿瘤的儿童建立临床改进平台
  • 批准号:
    10223903
  • 财政年份:
    2020
  • 资助金额:
    $ 81.75万
  • 项目类别:
Establishing a Platform for Clinical Improvement for Children with HIV-Associated Malignancies in Sub-Saharan Africa
为撒哈拉以南非洲地区患有艾滋病毒相关恶性肿瘤的儿童建立临床改进平台
  • 批准号:
    10657505
  • 财政年份:
    2020
  • 资助金额:
    $ 81.75万
  • 项目类别:
Pediatric HIV/AIDS & Infection-Related Malignancies Research Consortium for sub-Saharan Africa (PARCA)
儿童艾滋病毒/艾滋病
  • 批准号:
    10084671
  • 财政年份:
    2020
  • 资助金额:
    $ 81.75万
  • 项目类别:
Establishing a Platform for Clinical Improvement for Children with HIV-Associated Malignancies in Sub-Saharan Africa
为撒哈拉以南非洲地区患有艾滋病毒相关恶性肿瘤的儿童建立临床改进平台
  • 批准号:
    10427347
  • 财政年份:
    2020
  • 资助金额:
    $ 81.75万
  • 项目类别:
Mentored Clinical Research to Improve Outcomes for Pediatric Mature B Cell Lymphoma in Uganda
指导临床研究以改善乌干达儿童成熟 B 细胞淋巴瘤的治疗结果
  • 批准号:
    10621584
  • 财政年份:
    2020
  • 资助金额:
    $ 81.75万
  • 项目类别:
Pediatric HIV/AIDS & Infection-Related Malignancies Research Consortium for sub-Saharan Africa (PARCA)
儿童艾滋病毒/艾滋病
  • 批准号:
    10427340
  • 财政年份:
    2020
  • 资助金额:
    $ 81.75万
  • 项目类别:
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