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- Title
Machine Learning to Identify Patients at Risk of Inappropriate Dosing for Renal Risk Medications: A Critical Comment on Kaas-Hansen et al [Response to Letter].
- Authors
Kaas-Hansen, Benjamin Skov; Rodríguez, Cristina Leal; Placido, Davide; Thorsen-Meyer, Hans-Christian; Nielsen, Anna Pors; Dérian, Nicolas; Brunak, Søren; Andersen, Stig Ejdrup
- Abstract
Indeed, we used eGFR <=30 mL/min/1.73m SP 2 sp as one of the inclusion criteria (p. 214 in Kaas-Hansen et al[2]) and to operationalise the notion of inappropriate dosing (p. 214 and figure 1 in Kaas-Hansen et al[2]), in turn serving as a basis for the five actual outcomes: >0, >=1, >=2, >=3 and >=5 daily inappropriate doses. Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction.
- Subjects
DRUGS; CHRONIC kidney failure; ACUTE kidney failure; MACHINE learning
- Publication
Clinical Epidemiology, 2022, Vol 14, p765
- ISSN
1179-1349
- Publication type
Article
- DOI
10.2147/clep.s344435