# Using Machine Learning to Improve Readmission Prediction in Alzheimer's Disease and Related Dementia

> **NIH NIH K01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $130,140

## Abstract

Project Summary/Abstract
By 2060, approximately 14 million adults are expected to live with Alzheimer’s disease and related dementia
(ADRD). Although ADRD patients represent 10% of the general geriatric population, they account for 37% of
the direct healthcare expenditures. Compared to other older adults, ADRD patients are at a significantly higher
risk of hospitalization and unplanned 30-day hospital readmission (hereafter “readmission”). Readmissions are
costly and expose ADRD patients to expedited cognitive decline, premature institutionalization, and death.
Availability of a caregiver after hospital discharge is critical for ADRD patients to ensure adherence to diet,
medications, and follow-up appointments. There is a paucity of evidence examining readmission among the
ADRD population. Most risk-assessment tools (e.g. LACE Index) have poor discrimination power and lack
inclusion of influential medical and social features, and caregiver availability particular to ADRD patients. A
potential solution is to develop a risk tool using hospitals’ electronic health records (EHRs) because they
contain salient clinical and sociodemographic features as well as a wealth of information from physicians’,
nurses’ and social workers’ notes (unstructured EHRs data). The specific research aims for this proposal
are to (1) develop and validate a risk-assessment tool for predicting readmission among ADRD
patients; (2) examine the feasibility/acceptability and clinical/economic utility of the readmission risk-
assessment tool; and (3) develop a natural language processing (NLP) algorithm to extract information
on caregiver availability from unstructured EHRs (exploratory). We hypothesize that the predictive power
of our risk tool will be at least 20% higher than that of LACE Index (the current risk tool used in the Michigan
Medicine hospitals). To accomplish this project, my mentors and I have defined a set of targeted career goals
and educational training. My training aims include (1) gain familiarity with the clinical aspects of ADRD
(linked with Research Aim 1); (2) acquire methodological skills in machine learning and predictive
modeling (linked with Research Aim 1); (3) develop an understanding of the logistics of the ADRD
patient discharge and care transition processes (linked with Research Aim 2); and (4) gain proficiency
in NLP and algorithm validation (linked with Research Aim 3). By completion of this award, I will have used
EHRs and data science to develop a validated risk-assessment tool for readmission for hospitalized ADRD
patients. The results will enable efficient and targeted discharge planning to reduce readmission and wasteful
spending. It will also provide pilot data needed to apply for an R01 examining the optimization of discharge
process/location for hospitalized ADRD patients. This career development award will lay the foundation for me
to become a unique health economist specialized in efficient care transitions for ADRD patients.

## Key facts

- **NIH application ID:** 10263307
- **Project number:** 5K01AG068361-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Elham Mahmoudi
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $130,140
- **Award type:** 5
- **Project period:** 2020-09-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10263307

## Citation

> US National Institutes of Health, RePORTER application 10263307, Using Machine Learning to Improve Readmission Prediction in Alzheimer's Disease and Related Dementia (5K01AG068361-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10263307. Licensed CC0.

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