# Predictive analytics for cognitive decline and Alzheimer’s disease

> **NIH NIH K23** · ALBERT EINSTEIN COLLEGE OF MEDICINE · 2021 · $197,640

## Abstract

Project Summary/Abstract
Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a major global
healthcare burden. However, there is still no effective disease modifying therapy for AD and
clinical trials with the aim of preventing or stabilizing cognitive impairment have largely failed.
Decision making in both clinical practice and research is highly dependent on practical predictive
tools, which can effectively predict cognitive or functional outcomes in individuals. Such models
could be potentially used in clinical research to boost the power of trials by enrollment of
participants who are most likely to show disease progression during the trial’s timeframe.
Alternatively, these models could be used to identifying individuals who would benefit from
primary or secondary prevention once there are effective treatments for AD. In this project, we
aim to provide a framework for practical prediction of cognitive decline with aging and prodromal
AD, by applying a novel ML framework to multiple dimensions of data (demographics, genetic risk
scores, neuropsychological measures, structural MRI, and amyloid imaging). Our ultimate goal is
to arrive at a new “Machine Learning predictive framework for aging and AD” (ML4AD), comprised
of dimensions each of which each will add incremental value to the predictive models, hence
increasing the performance of predictive models while keeping the costs and burden of research
at a minimum. The candidate for this Mentored Patient-Oriented Career Development Award
(K23), Dr. Ali Ezzati, is a Neurologist whose career goal is to develop predictive tools to help
research and clinical decision making in cognitive aging and dementia. The proposed research
will leverage the rich clinical and biomarker dataset available from several ongoing international
studies, but will also provide a unique avenue of investigation for the candidate. The candidate's
career development will benefit from close mentorship and scientific guidance of outstanding
investigators in aging/AD neurobiology (Dr. Lipton), machine learning and computational
neuroscience (Dr. Davatzikos), and biostatistics (Dr. Hall). The findings from this study will inform
future secondary prevention trials, in which sensitive indicators of early AD will be necessary to
identify high-risk subjects and track early clinical decline. This work will serve as the foundation
to move forward in independent research focusing on development of predictive tools in AD and
related neurodegenerative disorders.
Key words: Alzheimer’s Disease, Dementia, Mild Cognitive Impairment, Cognitive neurology,
Artificial Intelligence, Machine Learning, Predictive Analytics, Longitudinal Cohort, Big Data

## Key facts

- **NIH application ID:** 10221583
- **Project number:** 5K23AG063993-02
- **Recipient organization:** ALBERT EINSTEIN COLLEGE OF MEDICINE
- **Principal Investigator:** Ali Ezzati
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $197,640
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10221583

## Citation

> US National Institutes of Health, RePORTER application 10221583, Predictive analytics for cognitive decline and Alzheimer’s disease (5K23AG063993-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10221583. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*