Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
ICD-10 and SNOMED CT Representations of Cognitive Decline in All of Us. |
| Συγγραφείς: |
Kudyba O; Department of Information Science and Media Studies, University of Bergen, Norway., Babic A; Department of Information Science and Media Studies, University of Bergen, Norway.; Department of Biomedical Engineering, Linköping University, Sweden. |
| Πηγή: |
Studies in health technology and informatics [Stud Health Technol Inform] 2026 Jun 29; Vol. 338, pp. 259-263. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991- |
| Ιατρικοί όροι (MeSH): |
Cognitive Dysfunction*/classification , Cognitive Dysfunction*/diagnosis , Systematized Nomenclature of Medicine* , International Classification of Diseases* , Electronic Health Records*, Humans ; United States ; Clustering Algorithms ; Cluster Analysis ; Female ; Principal Component Analysis ; Male |
| Περίληψη: |
Electronic health record phenotyping of cognitive decline may vary with the ontology used to encode diagnoses and findings. We compared ICD-10 and SNOMED CT representations of cognitive decline in the All of Us dataset. Separate ontology-specific feature sets were constructed and analyzed with k-means clustering as an exploratory method; clusters were summarized by condition burden, unique conditions, span, age, gender, and top-condition prevalence. Principal component analysis was used for visualization, and logistic regression was used to examine whether ontology-shaped features were informative for dementia classification. ICD-10 clusters were dominated by broad symptom-oriented terms, whereas SNOMED CT showed more differentiated groupings, including a normal-cognition cluster and a higher-burden dementia-related cluster. These findings suggest that ontology choice can influence latent phenotype structure and may affect the interpretability and performance of downstream classification in observational health data. |
| Contributed Indexing: |
Keywords: All of Us; Cognitive decline; ICD-10; SNOMED CT; classification; clustering |
| Entry Date(s): |
Date Created: 20260703 Date Completed: 20260703 Latest Revision: 20260703 |
| Update Code: |
20260703 |
| DOI: |
10.3233/SHTI260842 |
| PMID: |
42394004 |
| Βάση Δεδομένων: |
MEDLINE |