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]

Benjamin Skov Kaas-Hansen1 Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark;2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark;3 Section for Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, DenmarkCorrespondence[email protected]

https://orcid.org/0000-0003-1023-0371

Cristina Leal Rodríguez2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

https://orcid.org/0000-0002-3133-0630

Davide Placido2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

https://orcid.org/0000-0002-8327-8838

Hans-Christian Thorsen-Meyer2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark;4 Department of Intensive Care Medicine, Copenhagen University Hospital (Rigshospitalet), Copenhagen, Denmark

https://orcid.org/0000-0001-7002-3089

Anna Pors Nielsen2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

https://orcid.org/0000-0001-7903-5051

Nicolas Dérian5 Data and Development Support, Region Zealand, Denmark

https://orcid.org/0000-0002-4477-023X

Søren Brunak2 NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

https://orcid.org/0000-0003-0316-5866

Stig Ejdrup Andersen1 Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark

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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 [Response to 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 [Response to Letter]

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