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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...
Autores principales: | Meaney, Christopher, Escobar, Michael, Moineddin, Rahim, Stukel, Therese A., Kalia, Sumeet, Aliarzadeh, Babak, Chen, Tao, O'Neill, Braden, Greiver, Michelle |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Inc.
2022
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Materias: | |
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 |
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