# Predictive modeling of Alzheimer's Disease Related Dementias (ADRD) in the elderly population empowered by knowledge-driven data mining

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $305,916

## Abstract

PROJECT SUMMARY
In both the United States and globally, we are witnessing an unprecedented increase in the elderly
population (> 65 years) and the very elderly population (> 85 years). This demographic shift has led to a
significant rise in the number of individuals affected by age-related diseases and conditions, with a
particularly profound impact on cognitive decline, Alzheimer's disease, and related dementias (ADRD).
There is an urgent imperative to create more accurate and practical predictive models for these
conditions, usable across a wide range of clinical settings in the United States. Much of ADRD related
advances (e.g., risk factors such as lifestyles, cognitive signs and symptoms, latest treatments) is often
embedded in Pubmed literatures, which are not directly available for computational analysis and manual
extraction is very time consuming and costly. To the best of our knowledge, there are no investigations on
predictive models for dynamic ADRD risk assessment leveraging knowledge from literatures. Our current
parent grant is focusing on the development of a computable rare disease knowledge hub to accelerate
rare disease knowledge discovery. To respond to NOSI-AG-23-032,
this
administrative supplement
application will utilize the increasing availability of EHR and the graph embedding methodology created in
the parent proposal, alongside our proficiency in real-time and real-world risk modeling, to evaluate the
effectiveness, implementability, and clinical utility of dynamic ADRD risk modeling in elderly population. In
our preliminary work, we have investigated methods to extract entities and relationships from unstructured
text data and showcased GRU-D based architectures in real-time risk assessment of post surgical
complications (PSC) 1,2 and chronic kidney disease (CKD) 3,4 with a reasonable model explainability at
population level. We will further construct an ADRD knowledge hub (ADKH), and integrate the ADKH
with GRU-D based dynamic ADRD risk assessment to overcome both data quality issues and difficulties
in feature selection and representation of EHR data. Our specific aims include (1) construction of a
computable ADKH to accelerate ADRD knowledge discovery, and (2) performance evaluation, feasibility,
and model explainability research of gated GRU-D based models for ADRD risk assessment in the elderly
population, leveraging ADKH and EHR.

## Key facts

- **NIH application ID:** 10939985
- **Project number:** 3R01HG012748-02S2
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** HONGFANG LIU
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $305,916
- **Award type:** 3
- **Project period:** 2023-09-06 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10939985

## Citation

> US National Institutes of Health, RePORTER application 10939985, Predictive modeling of Alzheimer's Disease Related Dementias (ADRD) in the elderly population empowered by knowledge-driven data mining (3R01HG012748-02S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10939985. Licensed CC0.

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