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CORRECTION article

Front. Med., 09 August 2022
Sec. Nephrology
Volume 9 - 2022 | https://doi.org/10.3389/fmed.2022.964157

Corrigendum: Prediction model of immunosuppressive medication non-adherence for renal transplant patients based on machine learning technology

This article is a correction to:

  1. Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology

    1. Read original article
\nXiao Zhu,&#x;Xiao Zhu2,3†Bo Peng&#x;Bo Peng3†QiFeng Yi, QiFeng Yi1,2*Jia Liu,, Jia Liu1,2,3*Jin Yan,Jin Yan1,2
  • 1Nursing School of Central South University, Changsha, China
  • 2Nursing Department of Third Xiangya Hospital of Central South University, Changsha, China
  • 3Research Center of Chinese Health Ministry on Transplantation Medicine Engineering and Technology, The Third Xiangya Hospital, Central South University, Changsha, China

A corrigendum on
Prediction model of immunosuppressive medication non-adherence for renal transplant patients based on machine learning technology

by Zhu, X., Peng, B., Yi, Q., Liu, J., and Yan, J. (2022). Front. Med. 9:796424. doi: 10.3389/fmed.2022.796424

In our published article, there was an error in Table 2 as published. Table 2 used a scale Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS), which was authorized by the original developer Dr. De Geest. Dr. De Geest contacted us recently. He suggested that the Table 2 should be presented like their team. Therefore, we would like to replace Table 2. The corrected Table 2 and its caption appear below.

TABLE 2
www.frontiersin.org

Table 2. Adherence to IM measured by BAASIS.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: immunosuppressive medication, non-adherence, prediction model, renal transplant patients, machine learning technology

Citation: Zhu X, Peng B, Yi Q, Liu J and Yan J (2022) Corrigendum: Prediction model of immunosuppressive medication non-adherence for renal transplant patients based on machine learning technology. Front. Med. 9:964157. doi: 10.3389/fmed.2022.964157

Received: 08 June 2022; Accepted: 18 July 2022;
Published: 09 August 2022.

Edited and reviewed by: Hoon Young Choi, Yonsei University, South Korea

Copyright © 2022 Zhu, Peng, Yi, Liu and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: QiFeng Yi, 734591680@qq.com; Jia Liu, chucklejl@163.com

†These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.