TY  - JOUR
AU  - Oh, Eui Geum
AU  - Oh, Sunyoung
AU  - Cho, Seunghyeon
AU  - Moon, Mir
PY  - 2025
DA  - 2025/3/5
TI  - Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study
JO  - JMIR Med Inform
SP  - e56671
VL  - 13
KW  - machine learning
KW  - EHR
KW  - electronic health record
KW  - electronic medical record
KW  - EMR
KW  - artificial intelligence
KW  - readmission
KW  - nursing data
KW  - clinical decision support
KW  - prediction
KW  - predictive
KW  - discharge
KW  - admission
KW  - hospitalization
AB  - Background: Unplanned readmissions increase unnecessary health care costs and reduce the quality of care. It is essential to plan the discharge care from the beginning of hospitalization to reduce the risk of readmission. Machine learning–based readmission prediction models can support patients’ preemptive discharge care services with improved predictive power. Objective: This study aimed to develop a readmission early prediction model utilizing nursing data for high-risk discharge patients. Methods: This retrospective study included the electronic medical records of 12,977 patients with 1 of the top 6 high-risk readmission diseases at a tertiary hospital in Seoul from January 2018 to January 2020. We used demographic, clinical, and nursing data to construct a prediction model. We constructed unplanned readmission prediction models by dividing them into Model 1 and Model 2. Model 1 used early hospitalization data (up to 1 day after admission), and Model 2 used all the data. To improve the performance of the machine learning method, we performed 5-fold cross-validation and utilized adaptive synthetic sampling to address data imbalance. The 6 algorithms of logistic regression, random forest, decision tree, XGBoost, CatBoost, and multiperceptron layer were employed to develop predictive models. The analysis was conducted using Python Language Reference, version 3.11.3. (Python Software Foundation). Results: In Model 1, among the 6 prediction model algorithms, the random forest model had the best result, with an area under the receiver operating characteristic (AUROC) curve of 0.62. In Model 2, the CatBoost model had the best result, with an AUROC of 0.64. BMI, systolic blood pressure, and age consistently emerged as the most significant predictors of readmission risk across Models 1 and 2. Model 1, which enabled early readmission prediction, showed a higher proportion of nursing data variables among its important predictors compared to Model 2. Conclusions: Machine learning–based readmission prediction models utilizing nursing data provide basic data for evidence-based clinical decision support for high-risk discharge patients with complex conditions and facilitate early intervention. By integrating nursing data containing diverse patient information, these models can provide more comprehensive risk assessment and improve patient outcomes. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2025/1/e56671
UR  - https://doi.org/10.2196/56671
DO  - 10.2196/56671
ID  - info:doi/10.2196/56671
ER  -