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 Houlind1 Department of Clinical Research, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark;2 The Capital Region Pharmacy, Herlev, Denmark;3 Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark;4 Emergency Department, Copenhagen University Hospital – Amager & Hvidovre, Hvidovre, DenmarkCorrespondence[email protected]

https://orcid.org/0000-0003-4058-3012 View further author information

Esben Iversen1 Department of Clinical Research, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark

https://orcid.org/0000-0002-7558-9257 View further author information

Baker Nawfal Jawad1 Department of Clinical Research, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark

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Thomas Kallemose1 Department of Clinical Research, Copenhagen University Hospital – Amager and Hvidovre, Hvidovre, Denmark

https://orcid.org/0000-0002-7356-6481 View further author information

Mads Hornum5 Department of Nephrology, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark;6 Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

https://orcid.org/0000-0002-0123-4007 View further author information

References

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

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

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