Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care.
Saved in:
| Title: | Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care. |
|---|---|
| Authors: | Dong J; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA. junzi.dong@philips.com., Feng T; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA., Thapa-Chhetry B; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA., Cho BG; Connected Care and Personal Health Team, Philips Research North America, 222 Jacobs Street, Cambridge, MA, 02141, USA., Shum T; Department of Information Technology, Phoenix Children's Hospital, Phoenix, AZ, USA., Inwald DP; Paediatric Intensive Care Unit, Addenbrooke's Hospital, Cambridge, UK., Newth CJL; Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA, USA.; Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Vaidya VU; Department of Information Technology, Phoenix Children's Hospital, Phoenix, AZ, USA. |
| Source: | Critical care (London, England) [Crit Care] 2021 Aug 10; Vol. 25 (1), pp. 288. Date of Electronic Publication: 2021 Aug 10. |
| Publication Type: | Journal Article; Multicenter Study |
| Journal Info: | Publisher: BioMed Central Ltd Country of Publication: England NLM ID: 9801902 Publication Model: Electronic Cited Medium: Internet ISSN: 1466-609X (Electronic) Linking ISSN: 13648535 NLM ISO Abbreviation: Crit Care Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 1466-609X |
|---|---|
| DOI: | 10.1186/s13054-021-03724-0 |
