Identifying 20 homogeneous clusters
of acute patients discharged with nonspecific
diagnoses through k-prototypes mixed data
clustering

Identifying 20 homogeneous clusters
of acute patients discharged with nonspecific
diagnoses through k-prototypes mixed data
clustering

Acute medicine, Clustering, Emergency medicine, Nonspecific diagnoses, Noncausative diagnosis,
Unspecific diagnoses, Unsupervised machine learning

Background Patients discharged with nonspecific diagnoses after acute hospital care are frequent and represent
potential diagnostic uncertainty at discharge. Adverse outcomes indicate missed diagnoses with a potential for
improving patient safety. However, research and interventions are limited by population heterogeneity. We aimed to
identify clusters of patients discharged with nonspecific diagnoses by employing unsupervised machine learning and
to assess the risk of readmission and mortality of each cluster.
Methods Observational, register-based study of emergency department arrivals discharged with nonspecific
diagnoses (ICD-10: R and Z03 chapters) from March 2019 to February 2020 in Denmark. We applied partitional
(k-prototypes) and hierarchical (agglomerative) clustering based on demographics, socioeconomics, comorbidities,
administrative information, biochemistry, and 50 nonspecific discharge diagnosis groups. The risk of 30-day
readmission and mortality after discharge was assessed as cumulative incidence for each cluster.
Results We included 92,650 patients. A 20 clusters k-prototypes model best fitted our data. Clusters 1–5 were
differentiated by no or limited biochemistry across different age and comorbidity patterns. Clusters 6–9 consisted
mainly of young adults with low comorbidity, except Cluster 9 with notable neuropsychiatric and substance
abuse comorbidities. Clusters 10–20 described the older patients: 10–14 with single comorbidities and 15–20 with
substantial comorbidity of different cooccurring patterns. The risk of 30-day readmission and mortality ranged from
5% to 27% and 0% to 9% across clusters, respectively.
Conclusion Patients with nonspecific discharge diagnoses after acute hospital contacts can be grouped into 20
distinct clusters based on clinical, socioeconomic, administrative, and biochemical features. The clusters can be used
to form delimited populations allowing for better and more individualized prediction models.
Keywords Acute medicine, Clustering, Emergency medicine, Nonspecific diagnoses, Noncausative diagnosis,
Unspecific diagnoses, Unsupervised machine learning

Rasmus Gregersen Mottlau1,2,3* , Marie Villumsen2 , Axel Nyström4 , Hanne Nygaard1,3 , Jens Rasmussen1
,

Mikkel B. Christensen5,6,7 , Jakob Lundager Forberg8 and Janne Petersen2,3

https://doi.org/10.1186/s12873-025-01459-7

2026

PERI IRAWAN

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