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
- 来源:
- 关键词:2019-nCoVAdmission activityAdoptedAdoptionAlgorithmsAmbulatory CareArtificial IntelligenceAwardBiological AssayBloodCOVID-19COVID-19 diagnosticCOVID-19 patientCOVID-19 severityCaregiversCharacteristicsChildChildhoodClinicalCommunitiesCountryDataData AnalysesData CollectionData Coordinating CenterDevelopmentDiagnostic ProcedureDifferentiation AntigensDiseaseEmergency SituationEnsureFDA Emergency Use AuthorizationFamilyFutureImageImmuneIndividualInfectionInheritedLaboratoriesLiteratureMachine LearningMethodsMonitorMucocutaneous Lymph Node SyndromeMultisystem Inflammatory Syndrome in ChildrenParticipantPathway interactionsPatientsPediatric HospitalsPhasePhenX ToolkitPhysiologic MonitoringPoliciesPreparationProcessProgressive DiseasePsychological TransferPublishingRADx RadicalReadinessRecordsRiskRisk AssessmentSchoolsSerologySeveritiesSeverity of illnessSpeedSpottingsStratificationSystemTestingTexasTime Series AnalysisTimeLineTrainingTranslationsValidationWorkassay developmentbasebiomedical referral centerbiosignaturecase-basedchemokinecytokinedata integrationdata standardsdesignepigenomicsgenetic varianthemodynamicsheterogenous dataimprovedinflammatory markerinteroperabilitylearning progressionlearning strategymachine learning algorithmnext generationnovelpatient populationpediatric patientsprognosticprogramsradiomicsrepositoryresponsesocial health determinantstranscriptomicstreatment planning
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.
这项工作旨在描述儿科新冠肺炎的特征,并根据预测对新入院患者进行分层
(未来)疾病严重程度。这种分层有几个影响:立即改善治疗计划,以及
因为疾病的机制途径被发现,指导治疗。预测未来的严重程度将告知
门诊治疗;患者本身、他们的家人、其他照顾者/同居者,以及学校和
雇主。随着全国(乃至全世界)采取不同程度的“重新开放”,这样的预测将
告知社区处理儿科携带者的政策。根据我们的初步分析,我们断言
包括定量血清学炎症标志物(细胞因子/趋化因子图谱,
免疫图谱)、转录组学、表观基因组学、纵向生理监测、时间序列分析、成像
放射组学和临床观察,包括健康的社会决定因素,即使在早期也包含足够的信息
将疾病分为不同的感染阶段,并预测疾病的严重程度。我们提出了一种人工智能/机器
整合这个丰富且不同种类的数据集、描述疾病谱并识别的学习方法
预测进展性疾病严重程度的生物特征。为了便于翻译本文件中开发的方法
与广泛的用户社区合作,我们整合了翻译开发功能,以监督设计控制
处理并确保我们的方法准备就绪,以进行监管审查。纳入我们的时间表是适当的
旨在符合SARS紧急使用授权(EUA)计划的监管里程碑-
CoV-2诊断。
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)
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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:
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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