Non-negative matrix factorization temporal topic models and clinical text data identify COVID-19 pandemic effects on primary healthcare and community health in Toronto, Canada

OBJECTIVE: To demonstrate how non-negative matrix factorization can be used to learn a temporal topic model over a large collection of primary care clinical notes, characterizing diverse COVID-19 pandemic effects on the physical/mental/social health of residents of Toronto, Canada. MATERIALS AND MET...

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Detalles Bibliográficos
Autores principales: Meaney, Christopher, Escobar, Michael, Moineddin, Rahim, Stukel, Therese A., Kalia, Sumeet, Aliarzadeh, Babak, Chen, Tao, O'Neill, Braden, Greiver, Michelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Inc. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861144/
https://www.ncbi.nlm.nih.gov/pubmed/35202844
http://dx.doi.org/10.1016/j.jbi.2022.104034

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8861144/
https://www.ncbi.nlm.nih.gov/pubmed/35202844
http://dx.doi.org/10.1016/j.jbi.2022.104034

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