Identification of outcome-oriented progression subtypes from mild cognitive impairment to Alzheimer's disease using electronic health records
Open accessAbstract
Alzheimer’s disease (AD) is a complex heterogeneous neurodegenerative disease that requires an in-depth understanding of its progression pathways and contributing factors to develop effective risk stratification and prevention strategies. In this study, we proposed an outcome-oriented model to identify progression pathways from mild cognitive impairment (MCI) to AD using electronic health records (EHRs) from the OneFlorida+ Clinical Research Consortium. To achieve this, we employed the long short-term memory (LSTM) network to extract relevant information from the sequential records of each patient. The hierarchical agglomerative clustering was then applied to the learned representation to group patients based on their progression subtypes. Our approach identified multiple progression pathways, each of which represented distinct patterns of disease progression from MCI to AD. These pathways can serve as a valuable resource for researchers to understand the factors influencing AD progression and to develop personalized interventions to delay or prevent the onset of the disease.
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Various passive data types-physical activity, movement, driving, sleep, speech, eye tracking, facial expressions, technology use, writing, texting, and typing dynamics-have been shown to distinguish people with neurodegenerative disease from those without.
“For example, changes in physical activity and movement patterns, driving behavior, sleep patterns, everyday speech, eye tracking, facial expressions, technology use, writing, texting, and keyboard typing dynamics have all been shown to differentiate those with and without neurodegenerative disease.111–119”
From Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias (2026) · cites this paper as [116]
The Introduction and the section on constructing temporal trajectories describe clinical EHR data used to study progression from mild cognitive impairment to Alzheimer’s disease. Our copy does not report that the listed passive data types distinguish people with neurodegenerative disease from those without (searched for the listed data types and for passive-data differentiation). (LLM, conf=0.78)
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Cited by
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Zhou, M., Tang, A. S., Zhang, H., Xu, Z., Ke, A. M. C., Su, C., Huang, Y., Mantyh, W. G., Jaffee, M. S., Rankin, K. P., et al. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104820 · Cited by 9 · see citing works
Self-citationReview of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias
Breithaupt, A. G., Tang, A., Paolillo, E. W., Bibars, M., Johnson, E. C. B., Saloner, R., Possin, K. L., Windon, C. C., Hill-Jarrett, T. G., Giorgio, J., et al. (2026) · Seminars in Neurology · doi:10.1055/a-2744-9871 · Cited by 6 · see citing works
- Authors
- Xu, J., Yin, R., Huang, Y., Gao, H., Wu, Y., Guo, J., Smith, G. E., DeKosky, S. T., Wang, F., Guo, Y., & Bian, J.
- Affiliations
- University of Florida Health (US)
- University of Florida (US)
- Weill Cornell Medicine (US)
- Corresponding author
- Xu, Jie
- ORCID
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2023-08-04
- Volume
- 2023
- Pages
- 764-773
- PMC
- PMC10785946
- OpenAlex
- W4385644938
- Cited by
- 5
- References
- 3
- Citations by year
- 2026: 2 · 2025: 3
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- Florida Department of Health (23A09)
- National Institutes of Health (UL1TR001427, R01AG080991, R01AG076234)
- National Institute on Aging (R01AG080991, R01AG080624, R01AG076234)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, mild cognitive impairment, electronic health records, LSTM, agglomerative clustering, patient clustering, risk stratification, disease prevention
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