Gradient Boosting Tree Model Research Articles

Vehicle drivability evaluation based on PCA-SSA-XGBoost

In order to improve the efficiency and quality of vehicle drivability evaluation, a drivability evaluation model based on principal component analysis, extreme gradient boosting tree and sparrow optimization algorithm is proposed. In this paper, the power upshift of DCT vehicle is taken as a typical working condition, and 18 objective evaluation indicators under the power upshift working condition are studied and defined. The objective evaluation indicators are simplified by principal component analysis, their redundancy and blockiness are reduced, and the model input samples are optimized. The extreme gradient boosting tree model is trained to predict the subjective score of drivability, and the sparrow algorithm is used to optimize the core hyperparameters of the extreme gradient boosting tree to improve the accuracy and stability of the model. Road tests show that after the objective evaluation indicators are simplified by principal component analysis, the model evaluation accuracy reaches 97%, which is better than BPNN ( 90%), SVM ( 91%), ELM ( 92%) and SSA-XGBoost ( 95%). It is proved that the accuracy and stability of the proposed PCA-SSA-XGBoost model are better than other models, and it can complete the drivability evaluation more effectively. The evaluation model can be transferred and applied to other driving conditions, and has application value in solving the subjective and objective mapping problem in drivability evaluation.

Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer.

Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use readily available clinicopathologic variables. Patients with prostate adenocarcinoma who underwent surgery were identified in the National Cancer Database. Seven ML models were trained to predict organ-confined (OC) disease, extracapsular extension, seminal vesicle invasion (SVI), and lymph node involvement (LNI). Model performance was measured using area under the curve (AUC) on a holdout testing data set. Clinical utility was evaluated using decision curve analysis (DCA). Performance metrics were confirmed on an external validation data set. The ML-based extreme gradient boosted trees model achieved the best performance with an AUC of 0.744, 0.749, 0.816, 0.811 for the OC, ECE, SVI, and LNI models, respectively. The MSK nomograms achieved an AUC of 0.708, 0.742, 0.806, 0.802 for the OC, ECE, SVI, and LNI models, respectively. These models also performed the best on DCA. Findings were consistent on both a holdout internal validation data set as well as an external validation data set. Our ML models better predicted pathologic stage relative to existing nomograms at predicting pathologic stage. Accurate prediction of pathologic stage can help oncologists and patients determine optimal definitive treatment options for patients with prostate cancer.

Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach

Background/Objectives: Cerebrovascular events, such as strokes, are often preceded by the rupture of atherosclerotic plaques in the carotid arteries. This work introduces a novel approach to predict the occurrence of such events by integrating computational fluid dynamics (CFD), structural analysis, and machine learning (ML) techniques. The objective is to develop a predictive model that combines both imaging and non-imaging data to assess the risk of carotid atherosclerosis and subsequent cerebrovascular events, ultimately improving clinical decision-making. Methods: A multidisciplinary approach was employed, utilizing 3D reconstruction techniques and blood-flow simulations to extract key plaque characteristics. These were combined with patient-specific clinical data for risk evaluation. The study involved 134 asymptomatic individuals diagnosed with carotid artery disease. Data imbalance was addressed using two distinct approaches, with the optimal method chosen for training a Gradient Boosting Tree (GBT) classifier. The model’s performance was evaluated in terms of accuracy, sensitivity, specificity, and ROC AUC. Results: The best-performing GBT model achieved a balanced accuracy of 88%, with a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. This demonstrates the model’s high predictive power in identifying patients at risk for cerebrovascular events. Conclusions: The proposed method effectively combines CFD, structural analysis, and ML to predict cerebrovascular event risk in patients with carotid artery disease. By providing clinicians with a tool for better risk assessment, this approach has the potential to significantly enhance clinical decision-making and patient outcomes.

Leveraging a decade of Landsat-8 spectral records for mapping blue carbon storage in tidal salt marshes

Tidal salt marsh ecosystems are known to accumulate and store large amounts of “blue” carbon, making them an important component of regional carbon cycle processes and a potential target for ecosystem-based carbon crediting efforts. However, blue carbon content in salt marshes can vary substantially at relatively small spatial scales. Understanding spatial variations of blue carbon storage at the landscape or local scale is important for developing carbon inventories, guiding ecological restorations, and informing habitat management strategies. We investigated the potential of spectral index records from the Landsat-8 Operational Land Imager spanning from 2014 to 2023 for mapping blue carbon storage in the soils of two tidal salt marsh systems in the Mid-Atlantic United States. The decadal mean of a non-photosynthetic vegetation index and standard deviations of a normalized difference vegetation index and a modified normalized difference water index were identified as predictors of blue carbon. These predictors were used to train a gradient boosted trees model for predicting soil organic matter content that achieved a testing set r-squared value of 0.67. We estimated that the two study marshes stored a combined 133-208 gigagrams of organic carbon in the top 30 cm of soil. We emphasize the need for better quantification of deep soil carbon in tidal salt marsh systems, which is likely quite high, and demonstrate the potential for satellite-based mapping of blue carbon within individual tidal wetland systems.

Explainable Machine Learning Models to Predict Gibbs-Donnan Effect During Ultrafiltration and Diafiltration of High-Concentration Monoclonal Antibody Formulations.

Evaluating the Gibbs-Donnan and volume exclusion effects during protein ultrafiltration and diafiltration (UF/DF) is crucial in biopharmaceutical process development to precisely control the concentration of the drug substance in the final formulation. Understanding the interactions between formulation excipients and proteins under these conditions requires a domain-specific knowledge of molecular-level phenomena. This study developed gradient boosted tree models to predict the Gibbs-Donnan and volume exclusion effects for amino acids and therapeutic monoclonal antibodies using simple molecular descriptors. The models' predictions were interpreted by information gain and Shapley additive explanation (SHAP) values to understand the modes of action of the antibodies and excipients and to validate the models. The results translated feature effects in machine learning to real-world molecular interactions, which were cross-referenced with existing scientific literature for verification. The models were validated in pilot-scale manufacturing runs of two antibody products requiring high levels of concentration. By only requiring a molecule's biophysicochemical descriptors and process conditions, the proposed models provide an in silico alternative to conventional UF/DF experiments to accelerate process development and boost process understanding of the underlying molecular mechanisms through rational interpretation of the models.

Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction.

Accurate and efficient prediction of polymers properties is crucial for polymer design. Recently, data-driven artificial intelligence (AI) models have demonstrated great promise in polymers property analysis. Even with the great progresses, a pivotal challenge in all the AI-driven models remains to be the effective representation of molecules. Here we introduce Multi-Cover Persistence (MCP)-based molecular representation and featurization for the first time. Our MCP-based polymer descriptors are combined with machine learning models, in particular, Gradient Boosting Tree (GBT) models, for polymers property prediction. Different from all previous molecular representation, polymer molecular structure and interactions are represented as MCP, which utilizes Delaunay slices at different dimensions and Rhomboid tiling to characterize the complicated geometric and topological information within the data. Statistic features from the generated persistent barcodes are used as polymer descriptors, and further combined with GBT model. Our model has been extensively validated on polymer benchmark datasets. It has been found that our models can outperform traditional fingerprint-based models and has similar accuracy with geometric deep learning models. In particular, our model tends to be more effective on large-sized monomer structures, demonstrating the great potential of MCP in characterizing more complicated polymer data. This work underscores the potential of MCP in polymer informatics, presenting a novel perspective on molecular representation and its application in polymer science.

Enhancing Sustainability: Quantifying and Mapping Vulnerability to Extreme Heat Using Socioeconomic Factors at the National, Regional and Local Levels

Population-dense urban areas often concentrate high commercial and industrial activity and intricate transportation systems. In crowded cities, extreme events can be even more damaging due to the high population they affect and the social inequalities that are likely to emerge. Extreme heat is a climate hazard that has been linked to high morbidity and mortality, especially in cities with high population densities. The way extreme heat events are felt in the population varies depending on a variety of factors, such as age, employment status, living conditions, air-conditioning, housing conditions, habits, behaviors and other socio-demographic parameters. In this article, we quantify and locate vulnerabilities of populations to extreme heat in order to formulate policy and practice recommendations that will make communities resilient and will shape the transition to a more sustainable future. This work contributes towards the achievement of Sustainable Development Goal 11—Sustainable Cities and Communities—by developing the tools to make cities and settlements resilient and sustainable. To this end, we analyze socioeconomic data at the NUTS3 level for the national case study of Greece and at the census tract level for the local case study of the city of Athens. The target variable for this study is defined as the average daily mortality during heatwaves per 100,000 individuals, and a methodology is developed for constructing this variable based on socioeconomic data available in public databases. The independent variables were selected based on their contribution to socioeconomic vulnerability; they include the percentage of elderly individuals, retirees, unemployed persons, renters, those living alone, those residing in smaller houses, those living in older houses and immigrants from developing countries. An ensemble gradient boosted tree model was employed for this study to obtain feature importance metrics that was used to construct a composite index of socioeconomic heat vulnerability. The socioeconomic heat vulnerability index (SHVI) was calculated for each prefecture in Greece and for each census tract in the city of Athens, Greece. The unique feature of this SHVI is that it can be applied to any geographical resolution using the same methodology and produces a result that is not only quantifiable, but also facilitates a comparison between vulnerability scores across different regions. This application aimed to map the SHVI of both prefecture and city, to examine the significance of scale, to identify vulnerability hotspots, and rank the most vulnerable areas, which are prioritized by authorities for interventions that protect public health.

Elemental tracer determination and modeling for geographical origin designation of sweet oranges

Sweet oranges have long been an integral part of global health and culinary practices, offering a wealth of nutrients and bioactive compounds. Ensuring the authenticity of these citrus fruits is essential for maintaining consumer confidence, promoting transparency in sourcing, and protecting producers' reputations in the marketplace. In this study, we explored the feasibility of using multi-element profiling combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this, we employed an optimized microwave plasma atomic emission spectroscopy (MP-AES) method to analyze the elemental composition (Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sr, and Zn) of 183 orange samples from four production regions in northeastern Argentina. Support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to identify elemental tracer's indicative of origin. Based on a comprehensive evaluation of overall accuracy, receiver operating characteristic (ROC) curves, and area under the curve (AUC), the GBT model demonstrated the best classification performance, achieving a 96.5 % correct prediction rate on test samples, as confirmed by the ROC curve (AUC = 0.973). Consequently, this approach provides compelling evidence for the potential utility of MP-AES combined with supervised modeling to determine the geographic origin of sweet oranges produced in Argentina, thereby contributing to consumer protection against fraud.

Prediction of primary admission total charges following cervical disc arthroplasty utilizing machine learning

BACKGROUND CONTEXTCervical disc arthroplasty (CDA) has become an increasingly popular alternative to anterior cervical discectomy and fusion, offering benefits such as motion preservation and reduced risk of adjacent segment disease. Despite its advantages, understanding the economic implications associated with varying patient and hospital factors remains critical. PURPOSETo evaluate how hospital size, geographic region, and patient-specific variables influence charges associated with the primary admission period following CDA. STUDY DESIGNA retrospective analysis using machine learning models to predict and analyze charge factors associated with CDA. PATIENT SAMPLEData from the National Inpatient Sample (NIS) database from 2016 to 2020 was used, focusing on patients undergoing CDA. OUTCOME MEASURESThe primary outcome was total charge associated with the primary admission for CDA, analyzed against patient demographics, hospital characteristics, and regional economic conditions. METHODSMultivariate linear regression and machine learning algorithms including logistic regression, random forest, and gradient boosting trees were employed to assess their predictive power on charge outcomes. Statistical significance was set at the 0.003 level after applying a Bonferroni correction. RESULTSThe analysis included 3,772 eligible CDA cases. Major predictors of charge identified were hospital size and ownership type, with large and privately owned hospitals associated with higher charges (p<.001). The Western region of the U.S. also showed significantly higher charges compared to the Northeast (p<.001). The gradient boosting trees model showed the highest accuracy (AUC=85.6%). Length of stay and wage index were significant charge drivers, with each additional inpatient day increasing charges significantly (p<.001) and higher wage index regions correlating with increased charges (p<.001). CONCLUSIONSHospital size, geographic region, and specific patient demographics significantly influence the charges of CDA. Machine learning models proved effective in predicting these charges, suggesting that they could be instrumental in guiding economic decision-making in spine surgery. Future efforts should aim to incorporate these models into broader clinical practice to optimize healthcare spending and enhance patient care outcomes.

Predicting serious postoperative complications and evaluating racial fairness in machine learning algorithms for metabolic and bariatric surgery

BackgroundPredicting the risk of complications is critical in metabolic and bariatric surgery (MBS). ObjectivesTo develop machine learning (ML) models to predict serious postoperative complications of MBS and evaluate racial fairness of the models. SettingMetabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) national database, United States. MethodsWe developed logistic regression, random forest (RF), gradient-boosted tree (GBT), and XGBoost model using the MBSAQIP Participant Use Data File from 2016 to 2020. To address the class imbalance, we randomly undersampled the complication-negative class to match the complication-positive class. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score. Fairness across White and non-White patient groups was assessed using equal opportunity difference and disparate impact metrics. ResultsA total of 40,858 patients were included after undersampling the complication-negative class. The XGBoost model was the best-performing model in terms of AUROC; however, the difference was not statistically significant. While the F1 score and precision did not vary significantly across models, the RF exhibited better recall compared to the logistic regression. Surgery type was the most important feature to predict complications, followed by operative time. The logistic regression model had the best fairness metrics for race. ConclusionsThe XGBoost model achieved the highest AUROC, albeit without a statistically significant difference. The RF may be useful when recall is the primary concern. Undersampling of the privileged group may improve the fairness of boosted tree models.

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

This study introduces machine learning (ML) algorithms to predict hydrogen (H2) thermodynamic properties for geological storage, focusing on its mixtures with natural gas, nitrogen (N2), and carbon dioxide (CO2). Employing 1167 data samples, this research utilizes multiple linear regression (MLR), random forest (RF), gradient boosting (GB), and decision tree (DT) models, enhanced by sensitivity analysis for feature engineering. GB model's superior accuracy and efficiency over conventional statistical linear regression methods. The finding reveals that the GB model achieves superior performance, with an R2 value of 0.9998 for thermal capacity and the lowest mean absolute error (MAE)/root mean square error (RMSE) across all H2 properties—density (1.51%, 2.06 kg/m3), viscosity (0.000078%, 0.000117 cp), thermal conductivity (0.000427%, 0.000849 W/m.K), and thermal capacity (8.79%, 26.06 J/g.K). Moreover, this ML-based approach not only demonstrates remarkable accuracy and efficiency but also suggests the potential of smart paradigms to further enhance the safety and transportability of underground hydrogen storage projects. Ultimately, this work will help the reservoir simulation at field scale to be more robust and reliable.

Determining Air Quality Influential Parameters Using Machine Learning Techniques

Air quality is an important issue in public health and the environment. This research aims to develop an air quality prediction model based on PM10 and PM2.5 parameters using various regression and machine learning approaches. The dataset used includes air pollutant standard index (ISPU) data from a number of stations in the Jakarta area with an observation period from January to April 2024. The research method includes collecting datasets, reviewing literature and testing several models of machine learning techniques. Furthermore, the handling of outliers was carried out using the numeric outliers node and data normalization to prepare the data before dividing the training and testing data. The models evaluated include Linear Regression, Random Forest Regression, Gradient Boosted Trees, and Multilayer Perceptron (MLP), with validation using 10 times cross-validation. The results showed that the Random Forest Regression and Gradient Boosted Trees models provided good prediction performance for both PM10 and PM2.5 parameters. Random Forest Regression showed the lowest RMSE value on testing data for PM10 (0.048) and PM2.5 (0.037), while Gradient Boosted Trees showed the lowest RMSE value on training data for PM2.5 (0.032). The process of handling outliers and normalizing the data successfully improved the prediction accuracy of the model. Suggestions for future research include the exploration of new models, the addition of meteorological and socio-economic variables, and the application of models in real-time air quality monitoring systems.

Predicting distant metastasis in nasopharyngeal carcinoma using gradient boosting tree model based on detailed magnetic resonance imaging reports.

Development of distant metastasis (DM) is a major concern during treatment of nasopharyngeal carcinoma (NPC). However, studies have demonstrated improved distant control and survival in patients with advanced NPC with the addition of chemotherapy to concomitant chemoradiotherapy. Therefore, precise prediction of metastasis in patients with NPC is crucial. To develop a predictive model for metastasis in NPC using detailed magnetic resonance imaging (MRI) reports. This retrospective study included 792 patients with non-distant metastatic NPC. A total of 469 imaging variables were obtained from detailed MRI reports. Data were stratified and randomly split into training (50%) and testing sets. Gradient boosting tree (GBT) models were built and used to select variables for predicting DM. A full model comprising all variables and a reduced model with the top-five variables were built. Model performance was assessed by area under the curve (AUC). Among the 792 patients, 94 developed DM during follow-up. The number of metastatic cervical nodes (30.9%), tumor invasion in the posterior half of the nasal cavity (9.7%), two sides of the pharyngeal recess (6.2%), tubal torus (3.3%), and single side of the parapharyngeal space (2.7%) were the top-five contributors for predicting DM, based on their relative importance in GBT models. The testing AUC of the full model was 0.75 (95% confidence interval [CI]: 0.69-0.82). The testing AUC of the reduced model was 0.75 (95%CI: 0.68-0.82). For the whole dataset, the full (AUC = 0.76, 95%CI: 0.72-0.82) and reduced models (AUC = 0.76, 95%CI: 0.71-0.81) outperformed the tumor node-staging system (AUC = 0.67, 95%CI: 0.61-0.73). The GBT model outperformed the tumor node-staging system in predicting metastasis in NPC. The number of metastatic cervical nodes was identified as the principal contributing variable.

Estimation οf Wave Overtopping Discharges at Coastal Structures with Combined Slopes using Machine Learning Techniques

Coastal defense structures are of paramount importance in protecting coastal communities from the adverse impacts of severe weather events and flooding. This study uses machine learning techniques, specifically Decision Tree (DT), Gradient Boosted Tree (GBT), and Support Vector Machine (SVM) models, to estimate wave overtopping discharge at coastal structures with combined slopes employing the recently built EurOtop database. The models were evaluated by deploying statistical metrics and Taylor diagram visualization. The GBT model demonstrated a high level of accuracy in predicting wave-overtopping discharge. Compared to the other models, the scatter index of GBT (0.392) was lower than that of DT (0.512) and SVM (0.823). In terms of the R-index, GBT (0.991) was superior to DT (0.977) and SVM (0.943). The GBT results were also compared with those of previous works. The findings showed that the GBT model significantly decreased the overall error and provided accurate estimations of the wave-overtopping discharge.

Research on rolling bearing fault diagnosis method based on simulation and experiment fusion drive.

Aiming at the problem that the rolling bearing fault data are difficult to obtain and that the traditional fault diagnosis method does not consider the signal uncertainty characteristics and the low accuracy of models in the process of rolling bearing fault, a fault diagnosis method based on simulation and experiment fusion drive is proposed. First, the dynamics simulation model of rolling bearings under different fault conditions is established to obtain the bearing fault simulation signals. Second, a sequence generative adversarial network is used to fuse the simulation and experimental data. Bearing vibration signals are often very uncertain, so considering the probability characteristics of fault signals, the probability box model under different fault states is constructed by the direct probability box modeling method, and its characteristic vectors are extracted. Finally, an extreme gradient boosting Tree model for fault diagnosis classification is constructed to compare and evaluate the classification and diagnosis effects of bearing states before and after data fusion. The results show that the proposed method has a good diagnostic effect and is suitable for solving the fault diagnosis problem under the condition of insufficient data.

Spatiotemporal evolution and driving mechanisms of desertification on the Mongolian Plateau

Desertification poses a severe ecological and environmental challenge in the Mongolian Plateau (MP). It is difficult to quantify desertification distribution using unified indicators in the entire MP, because of its complex physical geographic conditions and various climatic zones covered. To accurately address this challenge, the spatial distribution of desertification at a 30-m resolution from 1990 to 2020 were mapped in this study. The desertification potential occurrence zone was identified by using a moisture index on the MP firstly. The feature space model and five machine learning models were constructed to make the map based on Google Earth Engine and Landsat data. The spatiotemporal distribution of desertification were further analyzed, and the dominant drivers of desertification distribution and evolution were identified using Geodetector model. The results indicate that the potential occurrence area of desertification accounted for 83.88 % of the total land area. The gradient boosted tree model for desertification assessment has the best performance with the highest overall accuracy of 88.18 %. The year 2010 marked a pivotal transition from land degradation to land restoration in the MP. Between 2010 and 2020, desertified land continued to deteriorate extensively in the southern Mongolia, while Inner Mongolia, China, essentially entered a full recovery phase. Precipitation and land use emerged as the primary drivers of the spatial distribution of desertification on the Mongolian Plateau and Mongolia, with potential evapotranspiration and precipitation influencing the distribution of desertification in Inner Mongolia, China. Land use change was the primary driver of desertification evolution on the MP and Mongolia. This study constructs an indicator system and methodology suitable for desertification monitoring on the MP, addresses the lack of refined desertification data over a long time series, and provides scientific reference for decision-making support in combating desertification in this region, and other large arid and semi-arid areas in the world.

Application of machine-learning model to optimize colonic adenoma detection in India.

There is limited data on the prevalence and risk factors of colonic adenoma from the Indian sub-continent. We aimed at developing a machine-learning model to optimize colonic adenoma detection in a prospective cohort. All consecutive adult patients undergoing diagnostic colonoscopy were enrolled between October 2020 and November 2022. Patients with a high risk of colonic adenoma were excluded. The predictive model was developed using the gradient-boosting machine (GBM)-learning method. The GBM model was optimized further by adjusting the learning rate and the number of trees and 10-fold cross-validation. Total 10,320 patients (mean age 45.18 ± 14.82years; 69% men) were included in the study. In the overall population, 1152 (11.2%) patients had at least one adenoma. In patients with age > 50years, hospital-based adenoma prevalence was 19.5% (808/4144). The area under the receiver operating curve (AUC) (SD) of the logistic regression model was 72.55% (4.91), while the AUCs for deep learning, decision tree, random forest and gradient-boosted tree model were 76.25% (4.22%), 65.95% (4.01%), 79.38% (4.91%) and 84.76% (2.86%), respectively. After model optimization and cross-validation, the AUC of the gradient-boosted tree model has increased to 92.2% (1.1%). Machine-learning models may predict colorectal adenoma more accurately than logistic regression. A machine-learning model may help optimize the use of colonoscopy to prevent colorectal cancers. ClinicalTrials.gov (ID: NCT04512729).

Deep Learning Diagnostic Classification of Cervical Images to Augment Colposcopic Impression.

A deep learning classifier that improves the accuracy of colposcopic impression. Colposcopy images taken 56 seconds after acetic acid application were processed by a cervix detection algorithm to identify the cervical region. We optimized models based on the SegFormer architecture to classify each cervix as high-grade or negative/low-grade. The data were split into histologically stratified, random training, validation, and test subsets (80%-10%-10%). We replicated a 10-fold experiment to align with a prior study utilizing expert reviewer analysis of the same images. To evaluate the model's robustness across different cameras, we retrained it after dividing the dataset by camera type. Subsequently, we retrained the model on a new, histologically stratified random data split and integrated the results with patients' age and referral data to train a Gradient Boosted Tree model for final classification. Model accuracy was assessed by the receiver operating characteristic area under the curve (AUC), Youden's index (YI), sensitivity, and specificity compared to the histology. Out of 5,485 colposcopy images, 4,946 with histology and a visible cervix were used. The model's average performance in the 10-fold experiment was AUC = 0.75, YI = 0.37 (sensitivity = 63%, specificity = 74%), outperforming the experts' average YI of 0.16. Transferability across camera types was effective, with AUC = 0.70, YI = 0.33. Integrating image-based predictions with referral data improved outcomes to AUC = 0.81 and YI = 0.46. The use of model predictions alongside the original colposcopic impression boosted overall performance. Deep learning cervical image classification demonstrated robustness and outperformed experts. Further improved by including additional patient information, it shows potential for clinical utility complementing colposcopy.

Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete

Abstract The development of nanotechnology has led to the creation of materials with unique properties, and in recent years, numerous attempts have been made to include nanoparticles in concrete in an effort to increase its performance and create concrete with improved qualities. Nanomaterials are typically added to lightweight concrete (LWC) with the goal of improving the composite’s mechanical, microstructure, freshness, and durability qualities. Compressive strength is the most crucial mechanical characteristic for all varieties of concrete composites. For this reason, it is essential to create accurate models for estimating the compressive strength (CS) of LWC to save time, energy, and money. In addition, it provides useful information for planning the construction schedule and indicates when the formwork should be removed. To predict the CS of LWC mixtures made with or without nanomaterials, nine different models were proposed in this study: the gradient-boosted trees (GBT), random forest, tree ensemble, XGBoosted (XGB), Keras, simple regression, probabilistic neural networks, multilayer perceptron, and linear relationship model. A total of 2,568 samples were gathered and examined. The most significant factors influencing CS during the modeling process were taken into account as input variables, including the amount of nanomaterials, cement, water-to-binder ratio, density, the content of lightweight aggregates, type of nano, fine and coarse aggregate content, and water. The performance of the suggested models was assessed using a variety of statistical measures, including the coefficient of determination (R 2), scatter index, mean absolute error, and root-mean-squared error (RMSE). The findings showed that, in comparison to other models, the GBT model outperformed the others in predicting the compression strength of LWC mixtures enhanced with nanomaterials. The GBT model produced the best results, with the greatest value of R 2 (0.9) and the lowest value of RMSE (5.286). Furthermore, the sensitivity analysis showed that the most important factor influencing the prediction of the CS of LWC enhanced with nanoparticles is the water content.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.