ArticleIBRO neuroscience reports2026
Data-driven subtyping of Alzheimer's emergency presentations using unsupervised machine learning.
Article in IBRO neuroscience reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Objective: To identify and describe age-specific multimorbidity subtypes among AD-associated ED encounters using unsupervised machine learning and heatmap-based diagnostic profiling. Methods: 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. Results: 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. Conclusion: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.