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

> **NIH NIH R61** · BAYLOR COLLEGE OF MEDICINE · 2022 · $778,430

## Abstract

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.

## Key facts

- **NIH application ID:** 10320488
- **Project number:** 4R61HD105593-02
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** CARL E ALLEN
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $778,430
- **Award type:** 4N
- **Project period:** 2021-01-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10320488

## Citation

> US National Institutes of Health, RePORTER application 10320488, AICORE-kids: Artificial Intelligence COVID-19 Risk AssEssment for kids (4R61HD105593-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10320488. Licensed CC0.

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