Using ICD-9 diagnostic codes for external validation of topic models derived from primary care electronic medical record clinical text data.
Health Informatics J
; 29(1): 14604582221115667, 2023.
Article
en En
| MEDLINE
| ID: mdl-36639910
ABSTRACT
Background/Objectives:
Unsupervised topic models are often used to facilitate improved understanding of large unstructured clinical text datasets. In this study we investigated how ICD-9 diagnostic codes, collected alongside clinical text data, could be used to establish concurrent-, convergent- and discriminant-validity of learned topic models. Design/Setting:
Retrospective open cohort design. Data were collected from primary care clinics located in Toronto, Canada between 01/01/2017 through 12/31/2020.Methods:
We fit a non-negative matrix factorization topic model, with K = 50 latent topics/themes, to our input document term matrix (DTM). We estimated the magnitude of association between each Boolean-valued ICD-9 diagnostic code and each continuous latent topical vector. We identified ICD-9 diagnostic codes most strongly associated with each latent topical vector; and qualitatively interpreted how these codes could be used for external validation of the learned topic model.Results:
The DTM consisted of 382,666 documents and 2210 words/tokens. We correlated concurrently assigned ICD-9 diagnostic codes with learned topical vectors, and observed semantic agreement for a subset of latent constructs (e.g. conditions of the breast, disorders of the female genital tract, respiratory disease, viral infection, eye/ear/nose/throat conditions, conditions of the urinary system, and dermatological conditions, etc.).Conclusions:
When fitting topic models to clinical text corpora, researchers can leverage contemporaneously collected electronic medical record data to investigate the external validity of fitted latent variable models.Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Clasificación Internacional de Enfermedades
/
Registros Electrónicos de Salud
Tipo de estudio:
Diagnostic_studies
/
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Health Informatics J
Año:
2023
Tipo del documento:
Article