Prediction of Heparin Dose during Continuous Renal Replacement Therapy Surgery by Using the Gradient Boosting Regression Model

Supervised learning-based assessment of office layout satisfaction in academic buildings

A prediction model that estimates mortality at admission to the intensive care unit (ICU) is of potential benefit to both patients and society. Logistic regression models like Simplified Acute Physiology Score 3 (SAPS 3) and APACHE are the traditional ICU mortality prediction models. With the emergence of machine learning (machine learning) and artificial intelligence, new possibilities arise to create prediction models that have the potential to sharpen predictive accuracy and reduce the likelihood of misclassification in the prediction of 30-day mortality. We used the Swedish Intensive Care Registry (SIR) to identify and include all patients ≥18 years of age admitted to general ICUs in Sweden from 2008 to 2022 with SAPS 3 score registered. Only data collected within 1 h of ICU admission was used. We had 153 candidate predictors including baseline characteristics, previous medical conditions, blood works, physiological parameters, cause of admission, and initial treatment. We stratified the data randomly on the outcome variable 30-day mortality and created a training set (80% of data) and a test set (20% of data). We evaluated several hundred prediction models using multiple ML frameworks including random forest, gradient boosting, neural networks, and logistic regression models. Model performance was evaluated by comparing the receiver operator characteristic area under the curve (AUC-ROC). The best performing model was fine-tuned by optimizing hyperparameters. The model's calibration was evaluated by a calibration belt. Ultimately, we simplified the best performing model with the top 1-20 predictors. We included 296,344 first-time ICU admissions. We found age, Glasgow Coma Scale, creatinine, systolic blood pressure, and pH being the most important predictors. The AUC-ROC was 0.884 in test data using all predictors, specificity 95.2%, sensitivity 47.0%, negative predictive value of 87.9% and positive predictive value of 70.7%. The final model showed excellent calibration. The ICU risk evaluation for 30-day mortality (ICURE) prediction model performed equally well to the SAPS 3 score with only eight variables and improved further with the addition of more variables. The ICURE prediction model predicts 30-day mortality rate at first-time ICU admission superiorly compared to the established SAPS 3 score.

Cardiometabolic multimorbidity (CMM) is a prevalent syndrome among middle-aged and older adults, significantly impairing quality of life and imposing substantial health and economic burdens on China's aging healthcare system. The development of timely predictive models is crucial for enabling early intervention. This study aimed to integrate multidimensional data from the China Health and Retirement Longitudinal Study (CHARLS) to develop an effective predictive model for assessing the five-year risk of CMM onset, thereby facilitating early intervention and management for individuals at risk in China. We analyzed data from the 2015 to 2020 CHARLS surveys, involving 5,388 middle-aged and older adults initially free of CMM. The dataset was randomly split into a training set (70%) and a validation set (30%). Key predictors were identified from 31 potential variables using LASSO regression with 10-fold cross-validation. Selected predictors underwent correlation analysis and were used to construct both Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR) models. Variable contributions in the XGBoost model were interpreted using SHapley Additive exPlanations (SHAP) values, while the LR model was visualized via a nomogram. The performance of the superior model was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis. LASSO regression screened significant variables from the initial 31 candidates. Correlation analysis ultimately identified nine key predictors: systolic blood pressure, BMI, fasting blood glucose, total cholesterol, triglycerides, uric acid, age, comorbidities, and pain. The logistic regression model demonstrated superior and more stable predictive performance on the validation set, with an area under the curve (AUC) of 0.732 (95% CI: 0.703-0.761). Calibration curves indicated reliable predictive accuracy, and decision curve analysis established the clinical net benefit across a range of risk thresholds. This study developed and validated a reliable, clinically applicable logistic regression model, supplemented by a nomogram, for assessing the five-year risk of CMM in Chinese middle-aged and older adults. The model effectively identifies high-risk individuals, supporting targeted early intervention and management strategies to alleviate the health and economic burdens associated with CMM in this population.

Hydrological simulations and predictions are vital aspects of hydrological change research. Accurate predictions of hydrological factors such as stage and discharge are essential for water resources planning, reservoir dispatching and operation, shipping management and flood control. River discharge forecasting during flood seasons is an important issue in water resources planning and management. To improve the calibration accuracy and stability of the stage–discharge relationship model, the feasibility of integrated algorithms for studying the stage–discharge relationship was explored. A random forest (RF) algorithm based on a neural network (NN) was developed using a framework of integrated algorithms. First, the Levenberg–Marquardt (LM) algorithm was used to optimise the weight updating process of a back-propagation (BP) NN and improve the convergence speed. Then, the LM-BP algorithm was used as a decision tree to build an RF algorithm. The model was tested using hydrological data from Hongqi station on the Dadu River in China in the flood season. Results for the classical model, BP NN model, LM-BP NN model and optimised algorithm model were evaluated based on the mean absolute error (MAE), mean square error (MSE) and mean absolute percentage error (MAPE). The performance indicators showed that the optimised algorithm model (MAE = 3.13 m3/s, MSE = 19.28 m3/s and MAPE = 1.8%) was superior to the other models and showed high accuracy and good stability in flood-season flow forecasting.

Reservoir permeability is a crucial parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Permeability can be conventionally estimated from traditional approaches such as core analysis and well-test data, which are time-consuming and expensive. Many scientists tried to estimate permeability from nuclear magnetic resonance (NMR) logs utilizing complex mathematical equations that may achieve imprecise approximation of the permeability values. Gradient boosting generates additive regression models by successively fitting a straightforward base learner to present pseudo-residuals using least squares at every iteration. The execution speed and accuracy of gradient boosting can be considerably enhanced by applying randomization process. Besides, the randomization process improves robustness against over fitting of the base learner. So, the novelty of the current study is the development of a Stochastic Gradient Boosting (SGB) regression model to predict the permeability of petroleum reservoirs based on well logs. Besides benchmark machine learning techniques are used to predict reservoir permeability from well logs. The correlation coefficient (R), relative absolute error (RAE), mean-absolute error (MAE), root mean-squared error (RMSE), and root relative squared error (RRSE) are utilized to check the overall fitting between measured permeability versus predicted ones. It is found that Stochastic Gradient Boosting model achieved overall better performance than the other models.

To develop and validate a standardized prediction model aiming at 1-year axial length elongation and to guide the orthokeratology lens practice. This retrospective study was based on medical records of myopic children treated with orthokeratology. Individuals aged 8-15years (n=1261) were included and divided into the primary cohort (n=757) and validation cohort (n=504). Feature selection was primarily performed to sort out influential predictors by high-throughput extraction. Then, the prediction model was developed using multivariable linear regression analysis completed by backward stepwise selection. Finally, the validation of the prediction model was performed by evaluation metrics (mean-square error, root-mean-square error, mean absolute error and ). No significant difference was found between primary and validation cohort (all p>0.05). After the feature selection, the crude model was adjusted by demographic information in multivariable linear regression analysis, and five final predictors were identified (all p<0.01). The interaction effect of age with 1-month change of zone-3mm flat K was detected (p<0.01); hence, two final prediction models were developed based on two age subgroups. The validation proved an acceptable performance. An effective multivariable prediction model aiming at 1-year axial length elongation was developed and validated. It can potentially help clinicians to predict orthokeratology efficacy and make valid adjustments. The influential variables revealed in this model can also provide designers directions to optimize the design of lens to improve the efficacy of myopia control.

Machine learning prediction of bio-polyol yields and hydroxyl values from acid-catalyzed liquefaction of lignocellulosic biomass

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

A CLINICAL MODEL FOR THE PREDICTION OF DIET CONTROLLED GDM

To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classification models of three classical algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF). 269 clinical IMRT plans were chosen retrospectively and the GPRs of a total of 2340 fields by the 2%/2mm standard at the threshold of 10% were collected for dosimetric verification using electronic portal imaging device (EPID). Subsequently, the plan complexity feature values of each field were extracted and calculated, and a total of 6 machine learning models (classification and regression models for three algorithms) were trained to learn the relation between 21 plan complexity features and GPRs. Each model was optimized by tuning the hyperparameters and ten-fold cross validation. Finally, the GPRs predicted by the model were compared with measured values to verify the accuracy of the model, and the evaluation indicators were applied to evaluate each model to find the algorithm with the best prediction performance. The RF algorithm had the optimal prediction effect on GPR, and its mean absolute error (MAE) on the test set was 1.81%, root mean squared error (RMSE) was 2.14%, and correlation coefficient (CC) was 0.72; SVM was the second and ANN was the worst. Among the classification models, the RF algorithm also had the optimal prediction performance with the highest area under the curve (AUC) value of 0.80, specificity and sensitivity of 0.80 and 0.68 respectively, followed by SVM and the worst ANN. All the three classic algorithms, ANN, SVM, and RF, could realize the prediction and classification of GPR. The RF model based on plan complexity had the optimal prediction performance which could save valuable time for quality control workers to improve quality control efficiency.

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

With the in-depth implementation of China’s “National Strategy for Building a Strong Transportation Network,” the scale of expressway construction has continued to expand. As a result, the number of high-fill and deep-cut subgrade projects under complex geological conditions has increased significantly, leading to a surge in landslide-related issues. Consequently, accurate prediction of slope displacement is of critical importance for early warning and prevention of landslide disasters. This study proposes a hybrid prediction model, VMD-MPA-LSSVM-LSTM (VMLL), which integrates Variational Mode Decomposition (VMD), Marine Predators Algorithm (MPA), Least Squares Support Vector Machine (LSSVM), and Long Short-Term Memory (LSTM) networks. Using monitoring data from the high-fill embankment slope at Hongtuyao as the research subject, the VMLL model is employed to predict slope displacement based on small-sample data. The objective is to provide a more accurate method for early warning of slope displacement. Firstly, the original monitoring data are decomposed into trend displacement components and fluctuation displacement components using Variational Mode Decomposition (VMD). Subsequently, the trend component and the fluctuation component are predicted using Least Squares Support Vector Machine (LSSVM) and Long Short-Term Memory (LSTM) networks, respectively. Finally, the Marine Predators Algorithm (MPA) is employed to optimize the hyperparameters of the predictive models. Based on this framework, a VMLL-based slope displacement prediction model is constructed. To verify the superiority of the VMLL model, a comparative analysis was conducted against LSSVM, LSTM, and the VMD-LSSVM-LSTM models. The results demonstrate that the VMLL model achieves the highest prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 0.4022%, a Mean Absolute Error (MAE) of 0.016 mm, a Root Mean Square Error (RMSE) of 0.0206 mm, a coefficient of determination (R²) of 94.08%, and a Variance Accounted For (VAF) of 96.5%. Compared with the other three models, the VMLL model reduces the MAPE, MAE, and RMSE by 30.41–67.62%, 30.38–67.40%, and 27.32–71.00%, respectively. Meanwhile, it improves the R² and VAF by 5.95–217.51% and 0.38–11.48%, respectively. These improvements clearly demonstrate that the VMLL model outperforms the other models, indicating its significant advantage over single prediction models. Furthermore, three additional datasets were used to evaluate the model’s performance. The average values of the performance evaluation metrics for these datasets were 0.6207% (MAPE), 0.0238 mm(MAE), 0.0273 mm(RMSE), 90.33%(R²), and 96.81%(VAF), respectively. These results demonstrate the high accuracy and strong robustness of the proposed model in predicting both horizontal displacement and vertical settlement of slopes, providing a reliable methodological framework for slope stability assessment and landslide disaster early warning.

Developing novel foams with tailored properties is a challenge. If properly addressed, efficient screening can potentially accelerate material discovery and reduce material waste, improving sustainability and efficiency in the development phase. In this work, we address this problem using a hybrid experimental and theoretical approach. Machine learning (ML) models were trained to predict the density of polylactide (PLA) foams based on their processing parameters. The final ML ensemble model was a linear combination of gradient boosting, random forest, kernel ridge, and support vector regression models. Comparison of the actual and predicted densities of PLA systems resulted in a mean absolute error of 30 kg·m−3 and a coefficient of determination (R 2) of 0.94. The final ensemble model was then used to explore the ranges of predicted density in the space of processing parameters (temperature, pressure, and time) and to suggest some parameter sets that could lead to low-density PLA foams. The new PLA foams were produced and showed experimental densities in the range of 36–48 kg·m−3, which agreed well with the corresponding predicted values, which ranged between 38 and 54 kg·m−3. The experimental–theoretical procedure described here could be applied to other materials and pave the way to more sustainable and efficient foam development processes.

INTRODUCTION: The aim of the current study is to develop and validate a prediction model for postpartum hemorrhage (PPH) based on antenatal and intrapartum risk factors in a diverse patient population at a tertiary safety-net center. METHODS: In this observational cohort of 10,025 pregnant women who delivered at Denver Health Medical Center (DHMC) between April, 2016 and March, 2019, ante- and intrapartum characteristics were compared between women with PPH and those without. A predictive multivariable model was estimated using logistic regression with a backwards stepwise approach eliminating variables when P&gt;.05 among the prediction cohort of women delivering before April 2018. The model was validated among women, who delivered after April, 2018. RESULTS: A total of 9,774 women met inclusion criteria, 6,525 in the prediction cohort and 3,249 in the validation cohort. The prevalence of PPH in the prediction and validation cohorts was 6.1% and 6.8%, respectively. Maternal age, maternal body mass index (BMI, kg/m2), parity, gestational age, history of prior cesarean, history of PPH, abnormal placentation, neonatal weight, chorioamnionitis, platelet count on admission and intrapartum magnesium administration were included in the final prediction model. The area under the curve (AUC) for the prediction model was 0.81 (95% CI 0.79-0.84) and the AUC for the validation cohort was 0.89 (95% CI 0.86-0.91). CONCLUSION: The model performed well and can be used to identify women at risk for PPH. Further studies are necessary to evaluate the effect of the model use on decreasing PPH and associated maternal morbidity.

Background:Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organs and systems. However, in addition to physical damage, patients with SLE have an increased risk of mental...