Journal of Korean Academy of Nursing

Research Paper

Development of a machine learning-based prediction model for early hospital readmission after kidney transplantation: a retrospective study: Hye Jin Chong, Ji-hyun Yeom; J Korean Acad Nurs 2025;55(4):528-542. Published online November 21, 2025; DOI: https://doi.org/10.4040/jkan.25030

Abstract PDF ePub: Purpose
This study aimed to develop and validate a machine learning-based prediction model for early hospital readmission (EHR) post-kidney transplantation.
Methods
The study was conducted at the organ transplantation center of a university hospital, utilizing data from 470 kidney transplant recipients. We built and trained four machine learning models and tested them to identify the strongest EHR predictors. Predictive performance was evaluated using confusion matrices and the area under the receiver operating characteristic curve (ROC AUC).
Results
Among the 470 kidney transplant recipients with a mean age of 46.1 ± 12.02 years, 322 (68.5%) were males, and 74 (15.7%) were readmitted within 30 days after kidney transplantation. In total, 241 (51.2%) recipients were found to have experienced EHR after applying the random over-sampling examples method. The random forest model achieved the best performance, with an ROC AUC of .87 (validation set) and .82 (test set). The 15 most important features were steroid pulse therapy (recipient), cerebrovascular accident (recipient), heart failure (recipient), male sex (donor), cardiovascular disease (recipient), weekend discharge (recipient), peritoneal dialysis (recipient) cerebrovascular accident as the cause of brain death (donor), current smoker (recipient), cardiac arrest (donor), previous kidney transplantation (recipient), age (donor), hypertension (donor), male sex (recipient), and dialysis duration (recipient).
Conclusion
Our framework demonstrated strong predictive interpretability. It can support appropriate and effective clinical decision-making by assisting transplant professionals in stratifying recipients based on their risk of EHR. prioritizing post-discharge care and follow-up for high-risk individuals, and allocating targeted interventions such as closer monitoring or education.

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Original Article

Reliability and Validity of Korean Version of Nursing Students’ Anxiety and Self-Confidence with Clinical Decision Making Scale: Mi Yu, Young Eun, KA White, KyungJa Kang; J Korean Acad Nurs 2019;49(4):411-422. Published online January 15, 2019; DOI: https://doi.org/10.4040/jkan.2019.49.4.411

Abstract PDF

Abstract Purpose

The purpose of this study was to adapt, modify, and validate the Nursing Anxiety and Self-Confidence with Clinical Decision-Making Scale (NASC-CDM^©) for Korean nursing students.

Methods

Participants were 183 nursing students with clinical practice experience in two nursing colleges. The construct validity and reliability of the final Korean version of the NASC-CDM^© were examined using exploratory and confirmatory factor analyses and testing of internal consistency reliability. For adaptation and modification, the instrument was translated from English to Korean. Expert review and a cross-sectional survey were used to test the instrument's validity.

Results

The Korean version of the NASC-CDM^© (KNASC-CDM) was composed of 23 items divided into four dimensions: (i) Listening fully and using resources to gather information; (ii) Using information to see the big picture; (iii) Knowing and acting; and (iv) Seeking information from clinical instructors. The instrument explained 60.1% of the total variance for self-confidence and 63.1% of the variance for anxiety; Cronbach's α was .93 for self-confidence and .95 for anxiety.

Conclusion

The KNASC-CDM can be used to identify anxiety and self-confidence in nursing students’ clinical decision-making in Korea. However, further research should be done to test this instrument, as it is classified differently from the original NASC-CDM^© version.

Citations

Citations to this article as recorded by

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