Gradient Boosting Decision Tree Algorithm Research Articles

Leveraging federated learning for boosting data privacy and performance in IVF embryo selection.

To study the effectiveness of federated learning in in vitro fertilization on embryo evaluation tasks. This is a retrospective cohort analysis. Two datasets were used in this study. The ploidy status dataset consisted of 10,065 embryo records, 3760 treatments, and 2479 infertile couples from 5 hospitals. The clinical pregnancy dataset consisted of 4495 embryo records, 4495 treatments, and 3704 infertile couples from 4 hospitals. Federated learning and the gradient boosting decision tree algorithm were utilized for modeling. On the ploidy status dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 71.78%, 73.10%, 69.39%, 69.72%, and 73.46% for 5 hospitals respectively, showing an average increase of 2.5% compared to those of our model trained without federated learning. On the clinical pregnancy dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 72.03%, 56.77%, 61.63%, and 58.58% for 4 hospitals respectively, showing an average increase of 3.08%. Federated learning can improve data privacy and data security and meanwhile improve the performance of embryo selection tasks by leveraging data from multiple sources. This study demonstrates the effectiveness of federated learning in embryo evaluation, and the results show the promise for future application.

A transfer learning strategy for tensile strength prediction in austenitic stainless steel across temperatures

Austenitic stainless steel plays a pivotal role in the nuclear industry with exceptional mechanical properties and corrosion resistance, wherein high-temperature tensile strength (∼290 °C) is a critical factor in ensuring its performance and reliability. This study focuses on the predictive modeling of tensile strength in cast austenitic stainless steel at room and elevated temperatures by developing a transfer learning strategy. The application of a gradient-boosting decision tree algorithm, enhanced by key features derived from room-temperature analyses and augmented with partial high-temperature data, results in significantly increased accuracy. The effective selection of features underscores the stability of critical variables across different temperatures, affirming the efficacy of the proposed transfer learning strategy. The work sheds light on material selection and design for high-temperature applications, and lays the groundwork for future exploration into predictive modeling of material properties in extreme conditions.

The impact of presentation modes on mental rotation processing: a comparative analysis of eye movements and performance

Mental rotation is the ability to rotate mental representations of objects in space. Shepard and Metzler’s shape-matching tasks, frequently used to test mental rotation, involve presenting pictorial representations of 3D objects. This stimulus material has raised questions regarding the ecological validity of the test for mental rotation with actual visual 3D objects. To systematically investigate differences in mental rotation with pictorial and visual stimuli, we compared data of N=54\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=54$$\\end{document} university students from a virtual reality experiment. Comparing both conditions within subjects, we found higher accuracy and faster reaction times for 3D visual figures. We expected eye tracking to reveal differences in participants’ stimulus processing and mental rotation strategies induced by the visual differences. We statistically compared fixations (locations), saccades (directions), pupil changes, and head movements. Supplementary Shapley values of a Gradient Boosting Decision Tree algorithm were analyzed, which correctly classified the two conditions using eye and head movements. The results indicated that with visual 3D figures, the encoding of spatial information was less demanding, and participants may have used egocentric transformations and perspective changes. Moreover, participants showed eye movements associated with more holistic processing for visual 3D figures and more piecemeal processing for pictorial 2D figures.

Accurate fall risk classification in elderly using one gait cycle data and machine learning

BackgroundFalls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. This challenge stems from individual variability and step-to-step fluctuations, making accurate classification difficult. MethodsWe recruited 44 participants, equally divided into high and low fall-risk groups. A smartphone secured on their second sacral spinous process recorded data during indoor walking. Features were extracted at each gait cycle from a 6-dimensional time series (tri-axial angular velocity and tri-axial acceleration) and classified using the gradient boosting decision tree algorithm. FindingsMean accuracy across five-fold cross-validation was 0.936. “Age” was the most influential individual feature, while features related to acceleration in the gait direction held the highest total relative importance when aggregated by axis (0.5365). InterpretationCombining acceleration, angular velocity data, and the gradient boosting decision tree algorithm enabled accurate fall risk classification in the elderly, previously challenging due to lack of discernible features. We reveal the first-ever identification of three-dimensional pelvic motion characteristics during single gait cycles in the high-risk group. This novel method, requiring only one gait cycle, is valuable for individuals with physical limitations hindering repetitive or long-distance walking or for use in spaces with limited walking areas. Additionally, utilizing readily available smartphones instead of dedicated equipment has potential to improve gait analysis accessibility.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.

Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Adult patients undergoing noncardiac surgery were retrospectively included in the study (76457 patients in the discovery cohort and 11910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Flight Trainee Performance Evaluation Using Gradient Boosting Decision Tree, Particle Swarm Optimization, and Convolutional Neural Network (GBDT-PSO-CNN) in Simulated Flights

Flight simulation training is one of the most important methods in early-stage civil aviation flight training. In this regard, flight simulation competitions are effective tools for evaluating the flight skills of trainees. In this study, a model is developed for evaluating the flight skills of trainees by integrating GBDT (Gradient Boosting Decision Tree), PSO (Particle Swarm Optimization), and CNNs (Convolutional Neural Networks). Flight data from simulations is employed for model training. Initially, performance data and scores are gathered from a simulated flight competition platform. The GBDT algorithm is then applied to filter and identify essential flight parameters from the collected data. Subsequently, the PSO-CNN model is utilized to train on the extracted flight parameters. The proposed GBDT-PSO-CNN model achieves a recognition rate of 93.8% on the test dataset. This assessment system is of significant importance for improving the specific maneuvering skill level of flight trainees.

Predicting and detecting fires on multispectral images using machine learning methods

In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.

Immediate postnatal prediction of death or bronchopulmonary dysplasia among very preterm and very low birth weight infants based on gradient boosting decision trees algorithm: A nationwide database study in Japan.

Bronchopulmonary dysplasia (BPD) poses a substantial global health burden. Individualized treatment strategies based on early prediction of the development of BPD can mitigate preterm birth complications; however, previously suggested predictive models lack early postnatal applicability. We aimed to develop predictive models for BPD and mortality based on immediate postnatal clinical data. Clinical information on very preterm and very low birth weight infants born between 2008 and 2018 was extracted from a nationwide Japanese database. The gradient boosting decision trees (GBDT) algorithm was adopted to predict BPD and mortality, using predictors within the first 6 h postpartum. We assessed the temporal validity and evaluated model adequacy using Shapley additive explanations (SHAP) values. We developed three predictive models using data from 39,488, 39,096, and 40,291 infants to predict "death or BPD," "death or severe BPD," and "death before discharge," respectively. These well-calibrated models achieved areas under the receiver operating characteristic curve of 0.828 (95% CI: 0.828-0.828), 0.873 (0.873-0.873), and 0.887 (0.887-0.888), respectively, outperforming the multivariable logistic regression models. SHAP value analysis identified predictors of BPD, including gestational age, size at birth, male sex, and persistent pulmonary hypertension. In SHAP value-based case clustering, the "death or BPD" prediction model stratified infants by gestational age and persistent pulmonary hypertension, whereas the other models for "death or severe BPD" and "death before discharge" commonly formed clusters of low mortality, extreme prematurity, low Apgar scores, and persistent pulmonary hypertension of the newborn. GBDT models for predicting BPD and mortality, designed for use within 6 h postpartum, demonstrated superior prognostic performance. SHAP value-based clustering, a data-driven approach, formed clusters of clinical relevance. These findings suggest the efficacy of a GBDT algorithm for the early postnatal prediction of BPD.

Residual facial erythema in atopic dermatitis patients treated with dupilumab stratified by machine learning.

Persistent facial erythema represents a significant complication in atopic dermatitis (AD) patients undergoing treatment with dupilumab. Stratifying patients based on the erythema course is crucial for elucidating heterogeneous phenotypes and facilitating advanced drug efficacy predictions. This study aimed to identify factors associated with facial erythema severity in dupilumab-treated AD patients and to establish a prediction model for drug response based on the identified factors. Data from a retrospective study conducted between July 2018 and July 2021 were collected and analysed. Patients were categorized into three groups via hierarchical clustering based on the course of facial erythema: early remission, low remission and persistent residual. LightGBM, a supervised gradient boosting decision tree algorithm, was employed to discern group differences and construct a prediction model. The model incorporated patient demographic and clinical profiles, including pre- and post-treatment examinations. The model's performance was evaluated using accuracy and the area under the receiver operating characteristic curve (AUC). The binary classification model demonstrated an accuracy of 89.10% and an AUC of 0.862 when distinguishing between early remission and persistent residual patients. The eight prominent factors associated with facial erythema severity included age, sex, lactate dehydrogenase (LDH), immunoglobulin E (IgE), eosinophil count, white blood cell count, Alnus allergy and cedar allergy. This study has two main significances: first, three clusters were identified through unsupervised learning; second, a classification model was constructed that proved more accurate than random prediction. The stratification and identification of crucial factors associated with residual facial erythema in dupilumab-treated AD patients lay the foundation for AI-powered prognostic models. This groundwork provides a substantial basis for enhancing future medical AI support in AD treatment selection, potentially improving personalized treatment approaches and outcomes.

Power Losses Reduction of Stacked Multicell Converter Fed Wind Energy Conversion System (WECS) Using Hybrid Methodology

This paper proposes a hybrid method for reduction of power losses of multicell converter in wind energy conversion system (WECS). The proposed methodology is the Artificial Intelligence (AI) based phase disposition pulse width modulation (PD-PWM). The Artificial Intelligence (AI) is the Gradient Boosting Decision Tree algorithm (GBDT). Hence it is named as GBDT-PD-PWM. Gradient boosting is a machine learning system that creates a prediction model in the group of weak prediction models, often called decision trees. The multicell converter is employed as the grid side converter (GSC) of WECS to attenuate harmonics created by nonlinear load. For stacked multicell converters (SMCs), a method of capacitor voltage balance based on PD-PWM approach is proposed.

Prediction and Diagnosis of Electric Vehicle Battery Fault Based on Abnormal Voltage: Using Decision Tree Algorithm Theories and Isolated Forest

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.

Application of Gradient Boosting Decision Tree Algorithm in Operation Quality Evaluation of Electric Energy Metering Device in Electric Power Company

Application of Gradient Boosting Decision Tree Algorithm in Operation Quality Evaluation of Electric Energy Metering Device in Electric Power Company

Hardness prediction of WC-Co cemented carbide based on machine learning model

The hardness of cemented carbides is a fundamental property that plays a significant role in their design, preparation, and application evaluation. This study aims to identify the critical factors affecting the hardness of WC-Co cemented carbides and develop a high-throughput predictive model for hardness. A dataset consisting of raw material composition, sintering parameters and characterization results of cemented carbides is constructed in which the hardness of cemented carbide is set as the target variable. By analyzing the Pearson correlation coefficient, Shapley additive explanations (SHAP) results, WC grain size and Co content are determined to be the key characteristics influencing the hardness of cemented carbide. Subsequently, machine learning models such as support vector regression (SVR), polynomial regression (PR), gradient boosting decision tree (GBDT), and random forest (RF) are optimized to construct prediction models for hardness. Evaluations using 10-fold cross-validation demonstrate that the GBDT algorithm model exhibits the highest accuracy and strong generalization capability, making it most suitable for predicting and analyzing the hardness of cemented carbides. Based on predictions from GBDT algorithm model, PR algorithm model is established to achieve high-precision interpretable prediction of the hardness of cemented carbides. As a result, a quantitative relationship between hardness and Co content and WC grain size is obtained, demonstrating that reducing grain size and Co content is the key to obtaining high hardness of cemented carbide. This research provides a data-driven method for accurately and efficiently predicting cemented carbide properties, presenting valuable insights for the design and development of high-performance cemented carbide materials.

Real‐time prediction and ponding process early warning method at urban flood points based on different deep learning methods

AbstractAccurate prediction of urban floods is regarded as one of the critical means to prevent urban floods and reduce the losses caused by floods. In this study, a refined prediction and early warning method system for urban flood and waterlogging processes based on deep learning methods is proposed. The spatial autocorrelation of rain and ponding points is analyzed by Moran's I (a common used statistic for spatial autocorrelation). For each ponding point, the relationship model between the rainfall process and ponding process is constructed based on different deep learning methods, and the results are analyzed and verified by mean absolute error (MAE), root mean square error (RMSE), Nash efficiency coefficient (NSE) and correlation coefficient (CC). The results show that the gradient boosting decision tree algorithm has the highest accuracy and efficiency (with a 0.001 m RMSE of the predicted and measured ponding depth) for ponding process prediction and is regarded as the most suitable method for ponding process prediction. Finally, the real‐time prediction and early warning of urban floods and waterlogging processes driven by rainfall forecast data are realized, and the results are verified by the measured data. The research results can provide theoretical support for urban flood prevention and control.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Establishment of a differential diagnosis method and an online prediction platform for AOSD and sepsis based on gradient boosting decision trees algorithm

ObjectiveThe differential diagnosis between adult-onset Still’s disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model.MethodsAll data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python.ResultsThe results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still’s disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/.ConclusionWe created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.

Abstract 13652: Machine Learning Prediction of RV End-Diastolic Volume in Patients With Pulmonary Regurgitation

Introduction: RV end-diastolic volume (RVEDV) by cardiac MRI (cMRI) guides intervention in congenital heart disease. Guideline directed linear and area measurements from two-dimensional echocardiography (2DE) have poor univariable linear correlation with RVEDV. We hypothesized that non-linear modeling of 2DE RV measurements may predict cMRI volume. Aims: We sought to develop a prediction algorithm for RVEDV from guideline-directed RV 2DE measurements. Methods: Patients from 2009 to 2022 with > mild pulmonary regurgitation and cMRI +/- 3 months were included. 2DE measurements were: RV end-diastolic (RVED) apical 4-chamber area, basal RVED diameter (d), mid RVED d, tricuspid annulus d, RVED length; PSLAX proximal RVOT d; PSSAX proximal RVOT d, distal RVOT d. A gradient-boosting decision trees algorithm (XGBoost) regressed cMRI RVEDV (mL) using 2DE inputs (including missing data) with leave-one-out crossvalidation. Predicted RVEDV was BSA-indexed to examine performance at clinical threshold 150 mL/m 2 . Intraclass correlation coefficient (ICC) in 30 samples was measured. Results: Of 243 subjects (median age 21 [IQR 16-31] y, 58% tetralogy of Fallot, 22% congenital pulmonary stenosis s/p intervention), 19% had cMRI RVEDV > 150 mL/m 2 . Missing ranged from 5% (basal RVED d) to 17% (PSSA proximal RVOT), and ICC ranged from 0.46 (basal RVED d) to 0.78 (tricuspid annulus). Mean absolute error was 40 mL (27 mL/m 2 ). At the predicted threshold 150 mL/m 2 had 39% sensitivity, 87% specificity, 40% positive predictive value, and 86% negative predictive value for true RV dilation. Conclusion: Prediction of RVEDV from guideline recommended 2DE RV measurements is possible. Detection of clinically significant RV dilation has low sensitivity but may be helpful in ruling out cases. Sources of error include interrater variability and missing data due to poor visualization of the RV on 2DE. Future work to develop a consistent and informative feature set for modeling is required.

Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects

Inter-basin water diversion is an essential means to alleviate the contradiction between the supply and demand of water resources, and accurate hydraulic modelling guarantees smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilities and equipment, it is difficult to obtain accurate and effective measured data to establish a model. Therefore, based on a data-driven method, this paper diagnoses and restores the important parameters of the water diversion projects, including the elevation of pipeline and water level data, which can be used to establish the adaptive hydraulic transition model of the water diversion projects. Firstly, the abnormal data of the elevation of pipeline were identified using expert data annotation and support vector classification (SVC), with the identification accuracy of abnormal data being as high as 91%. Then, the single and continuous abnormal data were restored using an interpolation method and multiple linear regression algorithm (MLR), and the restored data were found to be consistent with the judgment of expert knowledge. Secondly, K-medoids was used to classify the complex multi-dimensional water level data, combined with the 3-sigma method to identify the outliers in each class. The gradient boosting decision tree algorithm (GBDT) trained on normal data restored outliers in a predictive manner, and the mean absolute percentage error (MAPE) was 0.003%, 0.025% and 0.091% in each class, respectively, showing the best accuracy compared with other models.

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma

BackgroundNoninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients.MethodsA total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman’s rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively.ResultsThe T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821–0.936) and 0.911 (95%CI, 0.825–0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854–0.958) and 0.918 (95%CI, 0.831–0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709–0.863) and 0.782 (95%CI, 0.660–0.902)] in the both cohorts and achieved good calibration capability and clinical utility.ConclusionsCE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

Applied gradient boosting decision tree algorithms for accurate prediction of trihalomethanes: A case study in dual-sources drinking water distribution system in metropolitan

The complex hydraulic environment in dual-source drinking water distribution system (DWDS) in metropolitan puts water safety in an unknown situation, which requires systematic water quality monitoring and controlling to reduce the water risk during water distribution process. Given that, this study conducted a one-year sampling of trihalomethanes (THMs) and identified the hydraulic junction area in a real dual-source DWDS. For efficient prediction of THMs, several predictive frameworks were established via mechanistic algorithms and machine learning (ML). The results indicated that Gradient Boosting Decision Tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between various of features with R2 reaching 0.903, 0.908, 0.945 and 0.959 for trichloromethane (TCM), bromodichloromethane (BDCM), dibromomonochloromethane (DBCM) and tribromomethane (TBM), respectively. Shapley additive explanations (SHAP) analysis of GBDT models further identified water temperature, residual chlorine, water supply distance, and pH were key features affecting THMs formation in practical DWDS. Furthermore, the spatio-temporal distribution of THMs was simulated by GBDT models and identified the region might be exposed to high risk of the THMs. The simulation results indicated that the DBCM and TBM were more likely to form in the region nearly to DWTP-X, whereas in the region nearly to DWTP-Y, the TCM and BDCM were the dominant THMs species due to differences in raw water characteristics. Undoubtedly, this study provided novel insights into utilizing ML models to predict THMs levels in dual-source DWDS in the metropolitan.