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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
Research article (Journal of Biomedical Informatics, 2022) · cited 25× · AI/ML
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
Summary
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 is a scholarly article[1].
Key Facts
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's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). 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. Retrieved May 24, 2026, from https://4ort.xyz/entity/non-negative-matrix-factorization-temporal-topic-models-and-clinical-text-data-identify-covid-19-pandemic-effects-on-pri
MLA“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.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/non-negative-matrix-factorization-temporal-topic-models-and-clinical-text-data-identify-covid-19-pandemic-effects-on-pri.
BibTeX@misc{4ortxyz_non-negative-matrix-factorization-temporal-topic-models-and-clinical-text-data-identify-covid-19-pandemic-effects-on-pri_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{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}}, year = {2026}, url = {https://4ort.xyz/entity/non-negative-matrix-factorization-temporal-topic-models-and-clinical-text-data-identify-covid-19-pandemic-effects-on-pri}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): 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 — https://4ort.xyz/entity/non-negative-matrix-factorization-temporal-topic-models-and-clinical-text-data-identify-covid-19-pandemic-effects-on-pri (retrieved 2026-05-24)
