Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction-Reference-Cited by-同舟云学术

Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction

Published:2022-02 Issue: Volume:Volume 14 Page:213-223
ISSN:1179-1349
Container-title:Clinical Epidemiology
language:en
Short-container-title:CLEP

Author:

Kaas-Hansen Benjamin Skov,Leal Rodríguez Cristina^ORCID,Placido Davide^ORCID,Thorsen-Meyer Hans-Christian^ORCID,Nielsen Anna Pors^ORCID,Dérian Nicolas^ORCID,Brunak Søren^ORCID,Andersen Stig Ejdrup

Publisher

Informa UK Limited

Subject

Epidemiology

Reference52 articles.

1. Pattern and Predictors of Medication Dosing Errors in Chronic Kidney Disease Patients in Pakistan: A Single Center Retrospective Analysis

2. Renal Insufficiency and Medication in Nursing Home Residents

3. Development, implementation and outcome analysis of semi-automated alerts for metformin dose adjustment in hospitalized patients with renal impairment

4. Choice of Antibiotics, Pharmacokinetics, and Dose Adjustments in Acute and Chronic Renal Failure

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1. Traditional Methods Hold Their Ground Against Machine Learning in Predicting Potentially Inappropriate Medication Use in Older Adults;Value in Health;2024-07

2. Theranostic roles of machine learning in clinical management of kidney stone disease;Computational and Structural Biotechnology Journal;2023

3. 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];Clinical Epidemiology;2022-06

4. Machine Learning to Identify Patients at Risk of Inappropriate Dosing for Renal Risk Medications: A Critical Comment on Kaas-Hansen et al [Letter];Clinical Epidemiology;2022-06