Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records.
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| Title: | Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records. |
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| Authors: | Bolt H; London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. Hikaru.bolt@lshtm.ac.uk., Suffel A; London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK., Matthewman J; London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK., Sandmann F; London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.; European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden., Tomlinson L; London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK., Eggo R; London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. |
| Source: | BMC nephrology [BMC Nephrol] 2023 Aug 09; Vol. 24 (1), pp. 234. Date of Electronic Publication: 2023 Aug 09. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 100967793 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2369 (Electronic) Linking ISSN: 14712369 NLM ISO Abbreviation: BMC Nephrol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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