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Abstract Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were assessed using ML algorithms to predict outcomes. Data preparation addressed missing values and balancing methods corrected imbalances. Model building involved training-test splits, survival analysis, and ML algorithms like Random Survival Forest (RSF) and Gradient Boosting. Feature importance was examined, with patient risk stratification guiding survival analysis. The best-performing model, RSF with ROS resampling, achieved the highest mean AUC of 0.80, the lowest IBS of 0.11, and IPCW c-index of 0.79, maintaining strong predictive ability over time. Top predictors for in-hospital mortality included age, GCS, pupil condition, PTT, IPH, and Rotterdam score, with high variations in predictive abilities over time. A risk stratification cut-off value of 63.34 separated patients into low and high-risk categories, with Kaplan–Meier curves showing significant survival differences. Our high-performing predictive model, built on first-day features, enables time-dependent risk assessment for tailored interventions and monitoring. Our study highlights the feasibility of AI tools in clinical settings, offering superior predictive accuracy and enhancing patient care for TBI cases.

Abstract Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed using mean and median. Data preprocessing involved feature scaling and one-hot encoding. Survival analysis used filter (Cox-P, Cox-c) and embedded (RSF, LASSO) feature selection methods. Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. Random oversampling addressed data imbalance. Optimal hyperparameters were determined based on average C-index. Features importance was assessed using standardized regression coefficients and permutation importance for top models. Stability was evaluated using 5-fold cross-validation standard deviation. Finally, 1799 observations with 40 outcome predictors were included. RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. Top BC predictors were LT graft types, IBD in recipients, recipient’s BMI, recipient’s history of PVT, and previous LT history. For mortality, they were post-transplant AST, creatinine, recipient’s age, post-transplant ALT, and tacrolimus consumption. We identified BC and mortality risk factors, improving decision-making and outcomes.

Abstract Objectives To enhance the identification of individuals at risk of developing clinically significant kidney stones. Methods In this study, data from the Fasa Adults Cohort Study were analyzed to explore factors linked to symptomatic and clinically significant kidney stone disease. After cleaning, 10,128 participants with 103 variables were studied. One outcome variable (presence of symptomatic kidney stones) and 102 predictor variables from surveys and tests were assessed. Five Machine learning (ML) algorithms (SVM, RF, KNN, GBM, XGB) were applied to examine kidney stone factors, with performance comparisons made. Data balancing was done using SMOTE, and metrics like accuracy, precision, sensitivity, specificity, F1 score, and AUC were evaluated for each algorithm. Results The XGB model outperformed others with AUC of 0.60, while RF, GBM, SVC, and KNN had AUC values of 0.58, 0.57, 0.54, and 0.52. RF, GBM, and XGB showed good accuracy at 0.81, 0.81, and 0.77. Top predictors for kidney stones were serum creatinine, salt intake, hospitalization history, sleep duration, and BUN levels. Conclusions ML models show promise in evaluating an individual’s risk of developing painful kidney stones and recommending early lifestyle changes to reduce this risk. Further research can enhance predictive accuracy and tailor interventions for better prevention/management.

Abstract Objective To evaluate the frequency and significance of brain imaging findings in methanol poisoning patients and to propose a criterion for prioritizing brain imaging. Methods We retrospectively reviewed the data of 306 patients (286 men and 34 women, mean age 32.10 ± 9.9 years) with confirmed methanol poisoning who were admitted to two hospitals in Iran during the COVID‐19 pandemic. We analyzed their demographic, clinical, laboratory, and brain imaging data. Results The main brain computed tomography (CT) scan findings were hypodensity in the putamen (11.1%), cerebellar nuclei (8.2%), diffuse cerebral edema (7.5%), and intracranial hemorrhage (ICH; 1.6%). These findings were associated with blood pH, Glasgow Coma Scale (GCS), renal failure, bicarbonate, oxygen, carbon dioxide, potassium, and glucose levels (p