Machine Learning to Identify Patients at Risk of Inappropriate Dosing for Renal Risk Medications: A Critical Comment on Kaas-Hansen et al [Letter]

Machine Learning to Identify Patients at Risk of Inappropriate Dosing for Renal Risk Medications: A Critical Comment on Kaas-Hansen et al [Letter]

Morten Baltzer Houlind,1–4 Esben Iversen,1 Baker Nawfal Jawad,1 Thomas Kallemose,1 Mads Hornum5,6 1Department of Clinical Research, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark; 2The Capital Region Pharmacy, Herlev, Denmark; 3Department of Drug Design a...

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Bibliographic Details
Main Authors: Houlind MB, Iversen E, Jawad BN, Kallemose T, Hornum M
Format: Article
Language:English
Published: Dove Medical Press 2022-06-01
Series:Clinical Epidemiology
Online Access:https://www.dovepress.com/machine-learning-to-identify-patients-at-risk-of-inappropriate-dosing--peer-reviewed-fulltext-article-CLEP
Description
Summary:Morten Baltzer Houlind,1–4 Esben Iversen,1 Baker Nawfal Jawad,1 Thomas Kallemose,1 Mads Hornum5,6 1Department of Clinical Research, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark; 2The Capital Region Pharmacy, Herlev, Denmark; 3Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark; 4Emergency Department, Copenhagen University Hospital – Amager & Hvidovre, Hvidovre, Denmark; 5Department of Nephrology, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark; 6Department of Clinical Medicine, University of Copenhagen, Copenhagen, DenmarkCorrespondence: Morten Baltzer Houlind, Tel +45 28838563, Email morten.baltzer.houlind@regionh.dk View the original paper by Dr Kaas-Hansen and colleagues A Response to Letter has been published for this article
ISSN:1179-1349

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