Deep Learning-Based Prediction of Short-Term Acute Kidney Injury Risk in ICU Hypertensive Patients

Objective: Acute kidney injury (AKI) is a severe complication in hypertensive patients admitted to the intensive care unit (ICU), characterized by subtle early symptoms and the limited efficacy of traditional early warning methods. This study aims to develop and validate a deep learning model for predicting AKI risk within the next 6 hours in hypertensive ICU patients, identify the optimal algorithm, and determine key predictive features. Methods: A total of 2,856 hypertensive ICU admissions were retrospectively extracted from the MIMIC-III database (2008-2019). After applying inclusion criteria (age 18-89 years, confirmed hypertension) and exclusion criteria (ICU stay ≤48 hours, ≥20% missing data), 1,947 valid cases were included, exhibiting significant class imbalance with an AKI incidence rate of 28.4%. The dataset was randomly split into training (70%) and test (30%) sets using stratified sampling to preserve class distribution. Static features (e.g., age, gender, ethnicity, admission care type) and 14 hourly dynamic features (e.g., heart rate, blood pressure, serum creatinine, blood urea nitrogen; 14 clinical indicators in total) were extracted. The XGBoost algorithm was then employed to select 21 key features with a cumulative importance ≥95% from the initial feature set. Seven binary classification prediction models were constructed and compared: S5, GRU, LSTM, RWKV, Transformer, Logistic Regression, and Random Forest. For the deep learning models, key training hyperparameters were uniformly set: a batch size of 128 and a learning rate of 1e-4. Model performance was comprehensively evaluated using metrics including accuracy, precision, recall, F1 score, and AUC, with statistical significance assessed via paired t-tests (P < 0.05). The reliability of the results was further validated through XGBoost feature importance analysis and paired t-tests (P < 0.05). Results: After data preprocessing and feature selection, the S5 model demonstrated the best performance among the seven models. The S5 model achieved the best overall performance (accuracy 0.7206, precision 0.7343, recall 0.7353, F1-score 0.7246, AUC 0.7565), significantly outperforming the other models (P < 0.05). Notably, although the logistic regression model achieved an accuracy of 0.8328, its precision was only 0.0985, indicating severe overfitting. The Transformer model had a recall of merely 0.1912, limiting its clinical utility. Additionally, XGBoost feature importance analysis revealed that neurosurgical services, age, ethnicity, serum creatinine, blood urea nitrogen, and blood pressure change rate are core predictors for AKI. Conclusions: The lightweight S5 model offers optimal balance and clinical adaptability for real-time 6-hour AKI prediction in hypertensive ICU patients. Its high recall and robustness can effectively reduce the underdiagnosis of AKI, while the identified core predictive features provide a clear basis for screening high-risk populations and developing personalized intervention strategies. Collectively, these attributes position the S5 model as a promising candidate for integration into ICU bedside early warning systems to facilitate timely intervention.