IMPROVING ROAD SAFETY: SUPERVISED MACHINE LEARNING ANALYSIS OF FACTORS INFLUENCING CRASH SEVERITY

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

Machine-learning prediction of treatment response to stereotactic body radiation therapy in oligometastatic gynecological cancer: A multi-institutional study

Chronic pain around the temporomandibular joint (TMJ) and masticatory muscles is a primary symptom of temporomandibular disorders (TMD). However, the clinical significance of magnetic resonance imaging (MRI) features in predicting TMD-related pain remains unclear. This study aimed to develop and interpret machine learning (ML) models based on MRI characteristics for predicting pain severity in patients with TMD. The present retrospective study included 584 patients with TMD between January 2022 and December 2024, yielding a total of 755 TMJ MRI data sets. Pain severity was classified using the visual analogue scale (VAS). Demographic variables (age, sex) and MRI features-including lesion side, disc position, disc morphology, disc signal, disc perforation, bilaminar zone tear, joint space, joint effusion, condylar movement, bony changes and morphology/signal of the lateral pterygoid muscle-were collected. Eleven ML models based on demographic and MRI features were developed: logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), gradient boosting classifier (GBC), bagging classifier (BC), extremely randomised trees (ETC), decision tree classifier (DTC) and multilayer perceptron (MLP). Model performance was evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1 score. Precision-recall (PR) curves and calibration curves were plotted to assess discrimination and model calibration. Decision curve analysis (DCA) was conducted to evaluate the clinical net benefit across a range of threshold probabilities. Model interpretability was enhanced using Shapley Additive Explanations (SHAP), which quantified the contribution of each feature to individual predictions. Feature selection was conducted based on mean SHAP values, and separate LightGBM models were constructed using the Top 3, 5, and 9 most important features, as well as the full-feature set, for performance comparison. The data set was randomly divided into a training set (n = 604) and a test set (n = 151). Among the 11 ML models, the LightGBM model demonstrated the best predictive performance, with an AUC of 0.899, and was therefore identified as the optimal model. SHAP analysis identified age, disc position and condylar movement as the top three contributing features. Feature selection analysis indicated that selecting the top nine SHAP-ranked variables led to the highest diagnostic performance, with an AUC of 0.829. This study developed an interpretable, high-performing MRI-based ML model incorporating SHAP analysis to integrate imaging and clinical features for objective pain assessment, which may help identify high-risk TMD patients and guide personalised treatment strategies.

In recent years, a significant amount of research has focused on analyzing the effectiveness of machine learning (ML) models for malware detection. These approaches have ranged from methods such as decision trees and clustering to more complex approaches like support vector machine (SVM) and deep neural networks. In particular, neural networks have proven to be very effective in detecting complex and advanced malware. This, however, comes with a caveat. Neural networks are notoriously complex. Therefore, the decisions that they make are often just accepted without questioning why the model made that specific decision. The black box characteristic of neural networks has challenged researchers to explore methods to explain black-box models such as SVM and neural networks and their decision-making process. Transparency and explainability give the experts and malware analysts assurance and trustworthiness about the ML models’ decisions. In addition, it helps in generating comprehensive reports that can be used to enhance cyber threat intelligence sharing. As such, this much needed analysis drives our work in this paper to explore the explainability and interpretability of ML models in the field of online malware detection. In this paper, we used the Shapley Additive exPlanations (SHAP) explainability technique to achieve efficient performance in interpreting the outcome of different ML models such as SVM Linear, SVM-RBF (Radial Basis Function), Random Forest (RF), Feed-Forward Neural Net (FFNN), and Convolutional Neural Network (CNN) models trained on an online malware dataset. To explain the output of these models, explainability techniques such as KernalSHAP, TreeSHAP, and DeepSHAP are applied to the obtained results.

Accurate prediction of in-hospital mortality for patients with severe community-acquired pneumonia (SCAP) complicated by respiratory failure admitted to the intensive care unit (ICU) remains a critical challenge. This study aimed to develop and validate a machine learning (ML) model to predict this risk and compare its performance with conventional scoring systems. In this retrospective study, data from 164 patients with SCAP and respiratory failure admitted to the ICU between January 2017 and January 2024 were analyzed. Patients were randomly divided into a training set (n = 114) and a validation (test) set (n = 50). Forty-five clinical features collected at admission were used as candidate predictors. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for feature selection. Six ML models, including Gradient Boosting Decision Tree (GBDT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree(DT),Support Vector Machine (SVM), and Logistic Regression(LR), were constructed and evaluated.Use SHAP analysis to assess the contribution of each feature in a machine learning model. Construct a nomogram using the top six most influential features. The GBDT model demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI: 0.757-0.927) in the internal validation set, significantly outperforming the Acute Physiology and Chronic Health Evaluation II (APACHE-II, AUC = 0.70). Calibration curves demonstrated good agreement between predicted and observed mortality risks, particularly across the mid-probability range. Decision curve analysis indicated that the model provided a higher net benefit than "treat-all" and "treat-none" strategies across a broad range of threshold probabilities. SHapley Additive exPlanations (SHAP) analysis identified lactate, D-dimer, temperature, albumin, Prothrombin Time and Fraction of Inspired Oxygen as the six most influential predictors of in-hospital mortality. Based on these key predictors, we further developed a simplified nomogram to facilitate bedside risk estimation. The GBDT ML model, developed from routinely available clinical data, provides a highly accurate and clinically interpretable tool for predicting in-hospital mortality in SCAP patients with respiratory failure. It outperforms traditional severity scores and holds promise for assisting clinicians in risk stratification and early intervention.

We conduct an in-depth investigation of the structural, electronic, vibrational, thermodynamic, and thermoelectric characteristics of Na–Bi-based compounds, specifically tetragonal NaBi, hexagonal NaBi3, and cubic Na3Bi, using advanced first-principles calculations in conjunction with machine learning (ML) models. We used density functional theory (DFT) with spin–orbit coupling (SOC) to figure out the electronic structure, phonon dispersions, and thermoelectric transport using Boltzmann transport theory. Our findings validate the Dirac semimetal nature of cubic Na3Bi and demonstrate varied topological and thermodynamic properties within the Na–Bi family. To speed up the prediction of the thermoelectric figure of merit (ZT) while enhancing interpretability to understand at the feature level, we trained supervised ML models [Random Forest (RF) and Neural Network (NN)] on thermoelectric results from DFT. It is possible to directly compute the figure of merit (ZT) from DFT-derived transport coefficients such as the Seebeck coefficient, electrical conductivity, and thermal conductivity. However, machine learning (ML) models serve as powerful surrogate predictors, enabling rapid screening of derivative compounds and quantitative assessment of feature importance through SHAP (SHapley Additive exPlanations) analysis. At low temperatures, RF models consistently outperformed NN models, but both performed well at high temperatures. SHAP analysis showed that the Seebeck coefficient has the biggest effect on ZT in all regimes. This integrated, physics-informed, and data-driven methodology demonstrates that machine learning can significantly augment first-principles approaches. It accelerates predictions, guides feature prioritisation, and enhances design capabilities. The developed workflow provides a generalizable and interpretable framework for the predictive modeling of advanced topological thermoelectric materials.

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

This study examines the influence of two data preprocessing techniques, Robust Scaler (RS) and Principal Component Analysis (PCA), on the predictive behaviour of Machine Learning (ML) models used for triage assessment in Internet of Medical Things (IoMT)-based telemedicine environments to enhance the decision support for healthcare, where heterogeneous and high-dimensional clinical data often introduce challenges related to variability and analytical stability. The objective is to evaluate how RS and PCA contribute to the consistency and reliability of triage predictions when integrated with established ML algorithms. A dataset of 55,680 outpatient records was used to assess the integration of RS and PCA with five supervised ML models: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Naïve Bayes (NB). Performance was evaluated using accuracy, precision, recall, and F-score metrics. The evaluation reveals that each preprocessing technique affects model behaviour in distinct ways, and the RS Decision Tree combination exhibits stable performance in generating triage outcomes. PCA-based models exhibit characteristic patterns associated with dimensionality reduction, affecting interpretability and model response. The findings emphasize the role of preprocessing techniques in shaping ML-driven telemedicine workflows. Applying RS within ML pipelines supports consistent triage prediction, contributing to timely identification of patient conditions and strengthening data-driven remote healthcare services.

Harmful Algal Blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study aims to enhance the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, a proxy for algal blooms. The study employed multiple ensemble ML models, including Random Forest (RF), Deep Forest (DF), Gradient Boosting (GB), and XGBoost, and compared their performance against individual models such as Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP). The findings reveal that ensemble models, particularly XGBoost and Deep Forest (DF), achieve superior predictive accuracy with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAP) provided insights into the relative importance of input features, identifying Particulate Organic Nitrogen (PON), Particulate Organic Carbon (POC), and Total Phosphorus (TP) as critical factors influencing Chl-a concentrations. This research demonstrates the effectiveness of integrating ensemble ML models with XAI to improve HAB prediction accuracy and interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques in environmental monitoring.

BackgroundAcute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), with a mortality rate ranging from 15 to 20%. Accurate stratification of acute gastrointestinal bleeding into acute variceal gastrointestinal bleeding (AVGIB) and acute non-variceal gastrointestinal bleeding (ANGIB) subtypes is clinically essential as distinct entities require markedly different therapeutic approaches and even divergent prognostic implications. AUGIB characterized by hemorrhagic shock, hypotension, multiple organ dysfunction (MODS), and even circulatory failure is life-threatening. Machine learning (ML) prediction model can be an effective tool for mortality prediction, enabling the timely identification of high-risk patients and improving outcomes.MethodsA total of 3,050 acute upper gastrointestinal bleeding (AUGIB) patients were included in our research from the MIMIC-IV database, among which 625 patients were classified as AVGIB and 2,425 patients were categorized as ANGIB. Patients’ clinical features, intervention methods, vital signs, scores, and important laboratory results were collected. The Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN) and Adaptive Synthetic Sampling (ADASYN) were adopted to address the imbalance of the dataset. As many as 12 machine learning (ML) algorithms, namely, logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB), AdaBoost, XGBoost, Naive Bayes (NB), support vector machine (SVM), light gradient-boosting machine (LightGBM), K-nearest neighbors (KNN), extremely randomized trees (ET), and voting classifier (VC), were performed. The model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Shapley Additive exPlanations (SHAP) analysis was conducted to identify the most influential features contributing to mortality prediction.ResultsIn terms of AVGIB patients, extremely randomized trees model demonstrated excellent predictive value among other ML models, with the AUC of 0.996 ± 0.007, accuracy of 0.996 ± 0.009, precision of 0.957 ± 0.024, recall of 0.988 ± 0.012, and F1 score of 0.972 ± 0.007. The top 10 primary feature variables of ET model were whether combined with acute kidney failure, transfusion of albumin, vasoactive drugs, transfusion of plasma, transfusion of platelet, the max of international normalized ratio (INR), the max of prothrombin time (PT), and the max of activated partial thromboplastin time (APTT). In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. Similarly, the top 10 feature variables of GB model were Glasgow Coma Scale (GCS) score, vasoactive drugs, acute kidney failure, AIMS65 score, APACHE-II score, mechanical ventilation, the minimum of lactate, chronic liver disease, and the minimum and maximum of APTT. The SHAP visualization shows the weights of two ML models feature variables and the average sharp values of variables. Meanwhile, SHAP waterfall outputs the model prediction process with true positive and negative patients. Most importantly, two website prognostic prediction platforms were developed to enhance clinical accessibility: the ET model for AVGIB patients available at https://10zr656do5281.vicp.fun while the GB model for ANGIB patients accessible at http://10zr656do5281.vicp.fun.ConclusionThe ET model provides a reliable prognostic tool for AVGIB patients, while the GB model serves as a robust tool for ANGIB patients in predicting in-hospital mortality. By systematically integrating clinical features, risk stratification scores, vital signs, and invention measures, the ML models may deliver comprehensive predictions that benefit for clinical decision-making and potentially enhance clinical outcomes in the near future.

ObjectivesThis study aimed to develop and validate machine learning (ML) models to predict all-cause hospital admissions and 90-day readmissions using structured, patient-reported survey data.MethodsA cross-sectional survey was conducted between 3 July 2021 and 18 December 2022, among US adults aged ≥18 years with at least one cardiovascular risk factor. Participants were recruited through social media, community pharmacies and outpatient clinics. The final sample included 1318 participants. Primary outcomes were any all-cause hospitalisation and readmission within 90 days. Eight supervised ML models were trained using an 80:20 train–test split and 10-fold cross-validation. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), precision, recall, F1 score and calibration metrics. SHapley Additive exPlanations (SHAP) values identified key predictors.ResultsAmong 1318 participants, 35.0% reported at least one hospitalisation and 10.4% reported a 90-day readmission. The Extra Trees (ET) model demonstrated the best performance across both outcomes. For hospitalisation, ET achieved an AUROC of 0.93, precision of 0.83 and recall of 0.87. For readmission, AUROC was 0.99 with precision of 0.95 and recall of 0.96. SHAP analysis identified heart disease, medication burden, race/ethnicity, employment and insurance status as the most influential predictors.DiscussionPatient-reported data reflecting behavioural, social and clinical factors can predict hospitalisations with high accuracy, complementing traditional EHR-based models.ConclusionsIntegrating such patient-reported and behavioural data into electronic health records could enable earlier identification of high-risk individuals and support targeted, preventive interventions to improve healthcare outcomes.

Using AI to Predict Patient's Atypical Prescription Based on Individual's Feature-Set

Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis.

The use of machine learning (ML) models in forensic anthropology (FA) has increased in the last half decade; however, there is a lack of a standardized protocol on how to curate, use, and assess ML models. We introduce PUMAA (A Protocol for Utilizing Machine Learning in Forensic Anthropological Analyses), which includes a flowchart and a checklist that forensic practitioners can follow when creating, using, or assessing ML models for forensic research. The five factors that must be assessed in determining an ML model's true performance are explored. The most common types of supervised ML models are also explained in lay terms and accompanied by visuals to increase accessibility of these complex concepts. Although not exhaustive, examples of information that should be reported have been discussed for seven types of ML models. In addition, their various strengths and limitations are evaluated in order to equip researchers with the necessary tools to make decisions regarding when and how to use ML models. This protocol provides an initial standard for the use of ML in FA.

Extracorporeal membrane oxygenation-induced coagulopathy (ECMO-IC) represents a frequent and severe complication, contributing to oxygenator replacement and unfavorable outcomes. Currently, no reliable machine learning (ML) model exists for early identification. This study comprehensively assesses routine clinical characteristics to develop a reliable, accurate, and explainable ML model for estimating ECMO-IC risk and to identify modifiable factors. This study included two center cohorts with 266 patients undergoing ECMO from 2015 to 2024. Feature selection utilized the Boruta algorithm, followed by the implementation of a distinctive ML framework incorporating 12 ML algorithms to establish a consensus prediction model (ECMO-IC index). Model and feature variable assessment employed multiple analytical methods: Bootstrapping and fivefold cross-validation, subgroup and interaction analysis, restricted cubic spline (RCS) regression, and threshold effect analysis. Model interpretation and feature quantification relied on the Shapley Additive Explanations (SHAP) methodology for visualization purposes. Through Boruta algorithm selection, 17 characteristics were identified and incorporated into 12 ML methodologies, generating 105 permutations and an optimal algorithm for identifying ECMO-IC. The ECMO-IC index comprising 9 modifiable or nonmodifiable variables, namely platelet (PLT), lactate, systemic immune-inflammation index (SII), K, total protein (TP), shock index (SI), red blood cell volume distribution width (RDWCV), acute physiology and chronic health evaluation II (APACHE II), and Ca, demonstrated strong diagnostic capabilities, achieving a mean area under the curve (AUC) of 0.815 across derivation (AUC = 0.817) and validation (AUC = 0.813) cohorts, along with notable discriminatory power, model fit, and clinical utility. SHAP elucidates the importance of ranking features (PLT, lactate, K, Ca and APACHE II) and visualises global and individual ECMO-IC risk prediction. RCS regression and threshold effect analysis suggested a nonlinear link between model features (PLT: P for nonlinearity = 0.002, SII: P for nonlinearity = 0.001, K: P for nonlinearity = 0.006, Ca: P for nonlinearity = 0.008, and lactate: P for nonlinearity = 0.004) and ECMO-IC, and generated an inflection point for features (PLT = 95 × 109/L, lactate = 5.7mmol/L, SII = 200, K = 4.4mmol/L, TP = 45.6g/L, SI = 0.8, RDWCV = 14%, APACHE II = 15, Ca = 1.03mmol/L). To provide a more flexible predictive tool, the ECMO-IC model was constructed using a free, publicly available web-based calculator ( https://genglongliu.shinyapps.io/DynNomapp/ ). An optimised explainable ML model (ECMO-IC index) incorporating several modifiable parameters was established and internally validated to deliver an readily available and accurate diagnostic tool for ECMO-IC, with potential applications in ECMO clinical management.