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ICD-10 and SNOMED CT Representations of Cognitive Decline in All of Us.

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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
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  Data: ICD-10 and SNOMED CT Representations of Cognitive Decline in All of Us.
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  Data: <searchLink fieldCode="AU" term="%22Kudyba+O%22">Kudyba O</searchLink>; Department of Information Science and Media Studies, University of Bergen, Norway.<br /><searchLink fieldCode="AU" term="%22Babic+A%22">Babic A</searchLink>; Department of Information Science and Media Studies, University of Bergen, Norway.; Department of Biomedical Engineering, Linköping University, Sweden.
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  Data: <searchLink fieldCode="JN" term="%229214582%22">Studies in health technology and informatics</searchLink> [Stud Health Technol Inform] 2026 Jun 29; Vol. 338, pp. 259-263.
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  Data: <i>Original Publication</i>: Amsterdam ; Washington, DC : IOS Press, 1991-
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  Data: <searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+classification%22">classification</searchLink> <br /><searchLink fieldCode="MM" term="%22Cognitive+Dysfunction%22">Cognitive Dysfunction*</searchLink>/<searchLink fieldCode="MM" term="%22Cognitive+Dysfunction+diagnosis%22">diagnosis</searchLink> <br /><searchLink fieldCode="MM" term="%22Systematized+Nomenclature+of+Medicine%22">Systematized Nomenclature of Medicine*</searchLink> <br /><searchLink fieldCode="MM" term="%22International+Classification+of+Diseases%22">International Classification of Diseases*</searchLink> <br /><searchLink fieldCode="MM" term="%22Electronic+Health+Records%22">Electronic Health Records*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22United+States%22">United States</searchLink> ; <searchLink fieldCode="MH" term="%22Clustering+Algorithms%22">Clustering Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Cluster+Analysis%22">Cluster Analysis</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Principal+Component+Analysis%22">Principal Component Analysis</searchLink> ; <searchLink fieldCode="MH" term="%22Male%22">Male</searchLink>
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  Data: 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.
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  Data: <i>Keywords: </i>All of Us; Cognitive decline; ICD-10; SNOMED CT; classification; clustering
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  Data: <i>Date Created: </i>20260703 <i>Date Completed: </i>20260703 <i>Latest Revision: </i>20260703
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              Text: 2026 Jun 29
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