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Background <p>To identify distinct clinical phenotypes among older adults presenting to the emergency department (ED) using an unsupervised machine learning approach and to characterize their clinical characteristics, healthcare utilization patterns, and discharge outcomes.</p> Methods <p>The retrospective study was based on secondary analysis of data from the “Program for the Taiwan Triage and Acuity Scale (TTAS) for Elderly in Emergency Department”. A total of 1,617 non-traumatic older patients (aged ≥ 65 years) presenting to the emergency department (ED) were included. K-prototypes cluster analysis, an unsupervised machine learning approach, was used to identify distinct clinical phenotypes.</p> Results <p>The K-prototypes clustering algorithm identified three distinct phenotypes: Cluster 1 (frail and highly comorbid; 31.6%), Cluster 2 (younger and functionally preserved; 32.2%), and Cluster 3 (metabolically burdened; 36.2%). Cluster 1 was characterized by the highest median ED costs (4,191.5 points) and the longest LOS (median 8 days). Although 77.1% of patients in Cluster 2 were discharged from the ED, those requiring hospitalization had the highest proportion of in-hospital death or against advice discharge (AAD) (17.6%). Cluster 3 demonstrated obesity and metabolic risks but maintained relatively intact functional status.</p> Conclusions <p>Machine learning clustering identified distinct clinical phenotypes among older adults presenting to the ED. These phenotypes may provide additional insights into the heterogeneity of this population and could help inform future phenotype-oriented risk stratification and individualized emergency care.</p>

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Cluster analysis of elderly emergency department patients in Taiwan: identifying clinical profiles and healthcare utilization patterns

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Shou-Chien Hsu,
  • Cheng-Yu Chien,
  • Hsiao-Jung Tseng,
  • Yi-Chia Su

摘要

Background

To identify distinct clinical phenotypes among older adults presenting to the emergency department (ED) using an unsupervised machine learning approach and to characterize their clinical characteristics, healthcare utilization patterns, and discharge outcomes.

Methods

The retrospective study was based on secondary analysis of data from the “Program for the Taiwan Triage and Acuity Scale (TTAS) for Elderly in Emergency Department”. A total of 1,617 non-traumatic older patients (aged ≥ 65 years) presenting to the emergency department (ED) were included. K-prototypes cluster analysis, an unsupervised machine learning approach, was used to identify distinct clinical phenotypes.

Results

The K-prototypes clustering algorithm identified three distinct phenotypes: Cluster 1 (frail and highly comorbid; 31.6%), Cluster 2 (younger and functionally preserved; 32.2%), and Cluster 3 (metabolically burdened; 36.2%). Cluster 1 was characterized by the highest median ED costs (4,191.5 points) and the longest LOS (median 8 days). Although 77.1% of patients in Cluster 2 were discharged from the ED, those requiring hospitalization had the highest proportion of in-hospital death or against advice discharge (AAD) (17.6%). Cluster 3 demonstrated obesity and metabolic risks but maintained relatively intact functional status.

Conclusions

Machine learning clustering identified distinct clinical phenotypes among older adults presenting to the ED. These phenotypes may provide additional insights into the heterogeneity of this population and could help inform future phenotype-oriented risk stratification and individualized emergency care.