Using large administrative data for mining patients' trajectories for risk stratification: An exampl...
Using large administrative data for mining patients' trajectories for risk stratification: An example from urological diseases
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Publisher
United States: Public Library of Science
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Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
To identify latent clusters among urological patients by examining hospitalisation rate trajectories and their association with risk factors and outcome quality indicators.
Victorian Admitted Episodes Dataset, containing information on all hospital admissions in Victoria from 2009 to 2019. The top twenty ICD-10 primary diagnosis codes in urology...
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Full title
Using large administrative data for mining patients' trajectories for risk stratification: An example from urological diseases
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TN_cdi_plos_journals_3128165938
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_3128165938
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ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0310981
