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Data-driven subtyping of Alzheimer's emergency presentations using unsupervised machine learning
Alkam Tursun, Tarshizi Ebrahim, Van Benschoten Andrew H. IBRO neuroscience reports, 2026-09-06
Topic: Amyloid, tau and blood-test diagnosis
Older adults with Alzheimer's disease (AD) frequently present to emergency departments (EDs) with multiple coexisting conditions. However, age-specific patterns of multimorbidity in AD-related ED encounters remain incompletely characterized. To identify and describe age-specific multimorbidity subtypes among AD-associated ED encounters using unsupervised machine learning and heatmap-based diagnostic profiling. We conducted a cross-sectional analysis of the 2022 Nationwide Emergency Department Sample. AD-associated encounters were identified by an ICD-10-CM G30.x diagnosis code in any diagnosis field. The cohort included 125,461 ED encounters and was stratified into four age groups: 60-64, 65-74, 75-84, and ≥ 85 years. For each age group, the 30 most frequent co-occurring diagnoses were converted into binary indicators. KMeans clustering with eight clusters per age group was used for heatmap-based subtyping, while Uniform Manifold Approximation and Projection and Hierarchical Density-Based Spatial Clustering of Applications with Noise were used to visualize diagnostic structure. Distinct multimorbidity profiles were identified across all age groups. Among adults aged 60-74 years, clusters commonly included psychiatric and metabolic conditions, such as depression, anxiety, diabetes, chronic kidney disease, substance-use diagnoses, and dehydration. Among adults aged ≥ 75 years, clusters more frequently included cardiorenal disease, urinary tract infection, metabolic encephalopathy, respiratory failure, do-not-resuscitate status, and palliative care. The cohort had a mean age of 81.7 ± 7.2 years, and 62.6% of encounters involved women. Patterns of coexisting conditions in AD-related ED visits differed substantially by age. Adults aged 60-74 years more often showed psychiatric and metabolic combinations, whereas those aged ≥ 75 years more often showed infection, cardiorenal disease, respiratory failure, and end-of-life care markers. Recognizing these age-related patterns may help clinicians anticipate common care needs when evaluating patients with AD in the ED.
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APA 7th
Tursun, A., Ebrahim, T., & Van Benschoten Andrew, H. (2026). Data-driven subtyping of Alzheimer's emergency presentations using unsupervised machine learning. IBRO neuroscience reports. https://doi.org/10.1016/j.ibneur.2026.08.016
Vancouver
Tursun A, Ebrahim T, Van Benschoten Andrew H. Data-driven subtyping of Alzheimer's emergency presentations using unsupervised machine learning. IBRO neuroscience reports. 2026. doi:10.1016/j.ibneur.2026.08.016. PMID: 42698748.
BibTeX
@article{tursun2026-42698748,
author = {Tursun, Alkam and Ebrahim, Tarshizi and Van Benschoten Andrew, H.},
title = {{Data-driven subtyping of Alzheimer's emergency presentations using unsupervised machine learning}},
journal = {IBRO neuroscience reports},
year = {2026},
doi = {10.1016/j.ibneur.2026.08.016},
pmid = {42698748},
url = {https://doi.org/10.1016/j.ibneur.2026.08.016}
}
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Key findings
- Among adults aged ≥ 75 years, clusters more frequently included cardiorenal disease, urinary tract infection, metabolic encephalopathy, respiratory failure, do-not-resuscitate status, and palliative care.
- The cohort had a mean age of 81.7 ± 7.2 years, and 62.6% of encounters involved women.
- Patterns of coexisting conditions in AD-related ED visits differed substantially by age.
- Adults aged 60-74 years more often showed psychiatric and metabolic combinations, whereas those aged ≥ 75 years more often showed infection, cardiorenal disease, respiratory failure, and end-of-life care markers.
- Recognizing these age-related patterns may help clinicians anticipate common care needs when evaluating patients with AD in the ED.
Why it matters
This cross-sectional analysis of emergency department visits identified different patterns of coexisting conditions in people with Alzheimer's disease by age group. It describes emergency presentations and multimorbidity, not any treatment, cure, or prevention.
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Study details
- Study designcross-sectional
- Participants125,461
- Effect size62.6%
- Evidence strengthModerate
How this study scored 41/100
Published in a peer-reviewed journal, and it is a snapshot of people at one point in time. Scored zero for randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IBRO neuroscience reports) |
| Journal's citation record | 0 / 4 | IBRO Neuroscience Reports: journal h-index 29 (OpenAlex, 2026-10-29) |
| Study design | 4 / 12 | A snapshot of people at one point in time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 4 / 4 | 125,461 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-17) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 0 / 4 | Last-listed author Andrew H Van Benschoten: h-index 4 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of San Diego: institution h-index 225 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 42698748) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 41 / 100 | — |
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