Xiao Zhu
Bo Peng
QiFeng Yi
Jia Liu
Jin Yan1,2
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- 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
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.
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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, NzM0NTkxNjgwQHFxLmNvbQ==; Jia Liu, Y2h1Y2tsZWpsQDE2My5jb20=
†These authors have contributed equally to this work and share first authorship