Comparison of Methods for Estimating Temporal Topic Models From Primary Care Clinical Text Data: Retrospective Closed Cohort Study

BACKGROUND: Health care organizations are collecting increasing volumes of clinical text data. Topic models are a class of unsupervised machine learning algorithms for discovering latent thematic patterns in these large unstructured document collections. OBJECTIVE: We aimed to comparatively evaluate...

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Detalles Bibliográficos
Autores principales: Meaney, Christopher, Escobar, Michael, Stukel, Therese A, Austin, Peter C, Jaakkimainen, Liisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808604/
https://www.ncbi.nlm.nih.gov/pubmed/36534443
http://dx.doi.org/10.2196/40102

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808604/
https://www.ncbi.nlm.nih.gov/pubmed/36534443
http://dx.doi.org/10.2196/40102

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