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: 10272787
• 负责人: CARL E ALLEN
• 金额: $ 81.75万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10272787
• 关键词: 2019-nCoV; Admission activity; Adopted; Adoption; Algorithms; Ambulatory Care; Artificial Intelligence; Award; Biological Assay; Blood; COVID-19; COVID-19 diagnostic; COVID-19 patient; COVID-19 severity; Caregivers; Characteristics; Child; Childhood; Clinical; Communities; Country; Data; Data Analyses; Data Collection; Data Coordinating Center; Development; Diagnostic Procedure; Differentiation Antigens; Disease; 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; Psychological Transfer; Publishing; RADx Radical; Readiness; Records; Risk; Risk Assessment; Schools; Serology; Severities; Severity of illness; Speed; Spottings; Stratification; System; Testing; Texas; Time Series Analysis; TimeLine; Training; Translations; Validation; Work; assay development; base; 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; prognostic; programs; radiomics; repository; response; social health determinants; transcriptomics; treatment planning

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