# Unified and fair multimodal representation learning for autoimmune diseases

> **NIH NIH OT2** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $1,972,080

## Abstract

ABSTRACT
Autoimmune diseases affect 1 in 10 people. Commonly, patients needlessly suffer for years due to delays
in diagnosis and referral delays to specialists. Systemic lupus erythematosus is a classic example due to its
nonspecific symptoms and potential to mimic other diseases. It affects women 9 to 1 with average
diagnostic delays of over 5 years, increasing the chances of life-limiting end-organ damage. A diagnosis
typically requires an experienced rheumatologist to carefully consider and integrate various data sources.
This need for a specialist creates health equity concerns which are further compounded by the
disproportionately higher prevalence of lupus in Black and Hispanic women.
This project will develop a unified multimodal representation learning technology that will allow 1) using
many different datatypes (e.g., electronic health records, omics-data, full-body imaging, clinical
measures, tabular data, and data from activity monitors); 2) adding data sources to the multimodal model
as needed; 3) supporting missing modalities by cross-modal generative learning; 4) providing inherent
end-to-end interpretable results; and 5) patient-specific disease predictions and patient-personalized
multimodal information acquisition plans. The project will use a holistic design approach where a model
will account for population imbalances during training and model design and will incorporate debiasing
approaches directly into the modeling.
Our approaches will be generally applicable to multimodal learning. They target significantly earlier
diagnoses for autoimmune diseases, strategies to recommend suitable additional diagnostic tests, and the
ability to identify patients at greatest risk for the worst outcomes for which more aggressive treatments
may be recommended.

## Key facts

- **NIH application ID:** 11091113
- **Project number:** 1OT2OD038045-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Saira Z Sheikh
- **Activity code:** OT2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,972,080
- **Award type:** 1
- **Project period:** 2024-09-16 → 2026-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11091113

## Citation

> US National Institutes of Health, RePORTER application 11091113, Unified and fair multimodal representation learning for autoimmune diseases (1OT2OD038045-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11091113. Licensed CC0.

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