NDLI: Using ICD-9 diagnostic codes for external validation of topic models derived from primary care electronic medical record clinical text data

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Using ICD-9 diagnostic codes for external validation of topic models derived from primary care electronic medical record clinical text data

Content Provider	SAGE Publishing
Author	Meaney, Christopher Escobar, Michael Stukel, Therese A. Austin, Peter C. Kalia, Sumeet Aliarzadeh, Babak Moineddin, Rahim Greiver, Michelle
Copyright Year	2023
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.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/14604582221115667?download=true
ISSN	14604582
Issue Number	1
Volume Number	29
Journal	Health Informatics Journal (JHI)
e-ISSN	17412811
DOI	10.1177/14604582221115667
Language	English
Publisher	Sage Publications UK
Publisher Date	2023-01-13
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2023
Subject Keyword	clinical text data convergent validity ICD-9 codes concurrent validity topic model discriminant validity electronic medical record non-negative matrix factorization external validation
Content Type	Text
Resource Type	Article
Subject	Health Informatics

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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