Background: Acute kidney injury (AKI) can make cases of acute respiratory distress syndrome (ARDS) more complex, and the combination of the two can significantly worsen the prognosis. Our objective is to utilize machine learning (ML) techniques to construct models that can promptly identify the risk of AKI in ARDS patients. Method: We obtained data regarding ARDS patients from the Medical Information Mart for Intensive Care III (MIMIC-III) and MIMIC-IV databases. Within the MIMIC-III dataset, we developed 11 ML prediction models. By evaluating various metrics, we visualized the importance of its features using Shapley additive explanations (SHAP). We then created a more concise model using fewer variables, and optimized it using hyperparameter optimization (HPO). The model was validated using the MIMIC-IV dataset. Result: A total of 928 ARDS patients without AKI were included in the analysis from the MIMIC-III dataset, and among them, 179 (19.3%) developed AKI after admission to the intensive care unit (ICU). In the MIMIC-IV dataset, there were 653 ARDS patients included in the analysis, and among them, 237 (36.3%) developed AKI. A total of 43 features were used to build the model. Among all models, eXtreme gradient boosting (XGBoost) performed the best. We used the top 10 features to build a compact model with an area under the curve (AUC) of 0.850, which improved to an AUC of 0.865 after the HPO. In extra validation set, XGBoost_HPO achieved an AUC of 0.854. The accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and F1 score of the XGBoost_HPO model on the test set are 0.865, 0.813, 0.877, 0.578, 0.957 and 0.675, respectively. On extra validation set, they are 0.724, 0.789, 0.688, 0.590, 0.851, and 0.675, respectively. Conclusion: ML algorithms, especially XGBoost, are reliable for predicting AKI in ARDS patients. The compact model maintains excellent predictive ability, and the web-based calculator improves clinical convenience. This provides valuable guidance in identifying AKI in ARDS, leading to improved patient outcomes.

Background: Diabetes has emerged as a critical global public health crisis. Prediabetes, as the transitional phase with 5%-10% annual progression to diabetes, offers a critical window for intervention. The lack of a 5-year risk prediction model for diabetes progression among Chinese individuals with prediabetes limits clinical decision-making support. Objective: This study aimed to develop and validate a machine learning–based 5-year risk prediction model of progression from prediabetes to diabetes for the Chinese population and establish an interactive web-based platform to facilitate high-risk patients identifying and early targeted interventions, ultimately reducing diabetes incidence and health care burdens. Methods: A retrospective cohort study was conducted on 2 prediabetes cohorts from 2 Chinese medical centers (primary cohort: n=6578 and external validation cohort: n=2333) tracking from 2019 to 2024. Participants meeting the American Diabetes Association (ADA) criteria (prediabetes: hemoglobin A1c [HbA1c] level of 5.7%-6.4%; diabetes: HbA1c level of ≥6.5%) were identified. A total of 42 variables (demographics, physical measures, and hematologic biomarkers) were collected using standardized protocols. Patients were split into the training (70%) and test (30%) sets randomly in the primary cohort. Significant predictors were selected on the training set using recursive feature elimination methods, followed by prediction model development using 7 machine learning algorithms (logistic regression, random forest, support vector machine, multilayer perceptron, extreme gradient boosting machine, light gradient boosting machine, and categorical boosting machine [CatBoost]), optimized through grid search and 5-fold cross-validation. Model performance was assessed using the receiver operating characteristic curve, the precision-recall curves, accuracy, sensitivity, and specificity as well as multiple other metrics on both the test set and the external test set. Results: During the follow-up of 5 years, 2610 (41.6%) participants and 760 (35.2%) participants progressed from prediabetes to diabetes, with mean annual progression rates of 8.34% and 7.04% in the primary cohort and the external cohort, respectively. Using 14 features selected using the recursive feature elimination-logistic algorithm, the CatBoost model achieved optimal performance in the test set and the external test set with an area under the receiver operating characteristic curve of 0.819 and 0.807, respectively. It also showed the best discrimination performance on the accuracy, negative predictive value (NPV), and F1-scores as well as the calibration performances in both the test set and the external test set. Then the Shapley Additive Explanations (SHAP) analysis highlighted the top 6 predictors (FBG, HDL, ALT/AST, BMI, age, and MONO), enabling targeted modification of these risk factors to reduce diabetes incidence. Conclusions: We developed a 5-year risk prediction model of progression from prediabetes to diabetes for the Chinese population, with the CatBoost model showing the best predictive performance, which could effectively identify individuals at high risk of diabetes.

Retinal blood vessels segmentation plays an important role for retinal image analysis. In this paper, we propose robust retinal blood vessel segmentation method based on reinforcement local descriptions. A novel line set based feature is firstly developed to capture local shape information of vessels by employing the length prior of vessels, which is robust to intensity variety. After that, local intensity feature is calculated for each pixel, and then morphological gradient feature is extracted for enhancing the local edge of smaller vessel. At last, line set based feature, local intensity feature, and morphological gradient feature are combined to obtain the reinforcement local descriptions. Compared with existing local descriptions, proposed reinforcement local description contains more local information of local shape, intensity, and edge of vessels, which is more robust. After feature extraction, SVM is trained for blood vessel segmentation. In addition, we also develop a postprocessing method based on morphological reconstruction to connect some discontinuous vessels and further obtain more accurate segmentation result. Experimental results on two public databases (DRIVE and STARE) demonstrate that proposed reinforcement local descriptions outperform the state-of-the-art method.

Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors.Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors.Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model’s superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model’s applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846.Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas.

The atherogenic index of plasma (AIP) is recognized as a robust predictor of cardiovascular outcomes and diabetes incidence. However, no studies have comprehensively investigated the impact of baseline AIP levels and longitudinal AIP trajectories on diabetes progression in patients with prediabetes. After data inclusion and exclusion, this retrospective cohort study included 16,411 patients with prediabetes undergoing ≥3 health examinations (2019-2024). Latent Class Trajectory Modeling characterized longitudinal AIP trajectory patterns, and Cox proportional-hazards models evaluated associations of baseline AIP quartiles and trajectory groups with diabetes progression. During a mean 4-year follow-up (65,644 person-years), 14.14 % patients with prediabetes (2320) progressed to diabetes. Baseline AIP quartiles showed a graded relationship with diabetes progression risk. Three AIP trajectories were identified: the low plateau trajectory (N = 4422, 26.95 %), the medium decreasing trajectory (N = 8183, 49.86 %), and the high decreasing trajectory (N = 3806, 23.19 %). Compared to the low plateau trajectory, the medium decreasing trajectory, and the high decreasing trajectory exhibited progressively elevated diabetes risk in the fully adjusted model (HR = 1.42, 95 % CI: 1.22-1.65 and HR = 2.26, 95 % CI: 1.81-2.83, respectively). Patients with higher baseline AIP and higher trajectories are associated with higher risks for the progression from prediabetes to diabetes.