# Finding combinatorial drug repositioning therapy for Alzheimer's disease and related dementias

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2022 · $302,665

## Abstract

Summary
Artificial intelligence and machine learning (ML) models are becoming increasingly popular in
clinical applications. If we allow these “autonomous” ML models to make recommendations for
clinical decisions, it is important to ensure that they do introduce algorithmic unfairness (e.g.,
differences in the burden of disease or opportunities of treatment for different populations). We
propose novel technological solutions to mitigate algorithmic unfairness. We will address two
major types of data biases (subgroup and representation) to reduce their negative impact on ML
models. Based on contextual information and novel causal inference techniques, we will identify
potential outliers and task-irrelevant confounders and address them with customized mitigation
strategies (e.g., down-sampling and factor reduction) to avoid learning erroneous information
that might lead to health disparities. In addition, we will propose FairAUC (a new optimization
mechanism) to maximize prediction accuracy while considering fairness by design. As opposed
to post-hoc fairness rectification approaches, our method will automatically consider both
objectives in the training phase to strike the optimal balance between accuracy and fairness.

## Key facts

- **NIH application ID:** 10598207
- **Project number:** 3R01AG066749-03S1
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Xiaoqian Jiang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $302,665
- **Award type:** 3
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10598207

## Citation

> US National Institutes of Health, RePORTER application 10598207, Finding combinatorial drug repositioning therapy for Alzheimer's disease and related dementias (3R01AG066749-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10598207. Licensed CC0.

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