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：
10847803
负责人：
CARL E ALLEN
金额：
$ 156.59万
依托单位：
BAYLOR COLLEGE OF MEDICINE
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 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10847803
关键词：
2019-nCoV Admission activity Adopted Adoption Algorithms Ambulatory Care Antigens Artificial Intelligence Award Biological Assay Blood COVID-19 COVID-19 diagnostic COVID-19 patient COVID-19 risk COVID-19 severity Caregivers Characteristics Child Childhood Clinical Communities Country Data Data Analyses Data Collection Data Coordinating Center Dedications Development Diagnostic Procedure Disease Disease stratification Dryness Emergency Situation Ensure FDA Emergency Use Authorization Family Future Image Immune Individual Infection Inherited Laboratories Literature Machine Learning Methods Monitor Mucocutaneous Lymph Node Syndrome Multisystem Inflammatory Syndrome in Children Participant Pathway interactions Patients Pediatric Hospitals Phase PhenX Toolkit Physiologic Monitoring Policies Preparation Process Progressive Disease Publishing RADx Radical Readiness Records Risk Risk Assessment Schools Serology Severities Severity of illness Speed Spottings Stratification System Testing Texas Time Series Analysis Training Validation Work artificial intelligence algorithm assay development biomedical referral center biosignature case-based chemokine cytokine data integration data standards design epigenomics genetic variant hemodynamics heterogenous data improved inflammatory marker interoperability learning progression learning strategy machine learning algorithm next generation novel patient population pediatric patients prognostication programs radiomics repository response social health determinants timeline transcriptomics transfer learning translational approach treatment 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 COVID-19 的特征，并按预计的情况对入院患者进行分层（未来）疾病的严重程度有几个影响：立即改善治疗计划，以及随着疾病机制途径的揭示，预测未来的严重程度将告知疾病的风险。门诊治疗；患者本人、其家人、其他护理人员/同居者以及学校和随着全国（乃至全世界）采取不同程度的“重新开放”，这种预测将会发生。根据我们的初步分析，我们断言，有关处理社区儿科携带者的政策。包括定量血清学炎症标记物（细胞因子/趋化因子谱、免疫谱）、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我们提出了一种人工智能/机器来对感染的阶段进行分层并预测疾病的严重程度。学习方法来整合这个丰富且异构的数据集，描述疾病谱并识别预测进展性疾病严重程度的生物特征，以促进本研究中开发的方法的转化。与广泛的用户社区合作，我们整合了转化开发功能，以监督设计控制流程并确保我们的方法已准备好接受监管审查。旨在符合针对 SARS 有效的紧急使用授权 (EUA) 计划的监管里程碑 CoV-2 诊断。