Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models.

We propose a new method for learning ensembles of process-based models for predictive modeling of dynamic systems from data and knowledge. Previous work has shown that ensembles based on sampling data (i.e., bagging), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial computational overhead needed for learning. On the other hand, methods for constructing ensembles based on sampling knowledge (i.e., random library samples, RLS) allow for efficient learning ensembles of process-based models, while maintaining their long-term predictive performance. This paper aims at checking the conjecture whether the combination of these methods has a potential for further performance improvements. The proposed method, bagging of random library samples for learning ensembles of process-based models combines the afore-mentioned approaches in terms of sampling both data and knowledge. We apply the method to and evaluate its performance on a set of automated predictive modeling tasks in two lake ecosystems from data and library of knowledge for modeling population dynamics. The experimental results serve both to identify the optimal design decisions regarding the proposed method as well as to asses its predictive ability. The results show that such ensembles outperform single process-based model, but also outperform each of the two methods for learning ensembles from data samples (bagging) and knowledge samples (RLS).

Storing metadata about models of dynamical systems in a machine readable form is one of the key steps towards their accessibility and reusability. In the domain of process-based modeling of dynamical systems, the task is to construct an explanatory model of a dynamical system from domain knowledge and data. In this paper, we present a workflow for annotation, storage and querying of process-based models specifically in the domain of aquatic ecosystems. To provide the vocabulary of key terms about the process-based modeling paradigm, we use the Ontology for Process-Based Modeling of Dynamical Systems (OntoPBM). Next, to capture the domain-specific characteristics, we extend OntoPBM with terms specific for aquatic ecosystems. The annotations for each process-based model are stored in an RDF triple store. This enables us to execute SPARQL queries on facts asserted in the annotations, as well as facts inferred from the domain knowledge encoded in the ontology. Finally, by following the proposed workflow, we generate the minimal information about a model, which takes us one step closer towards reusable research.

Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models and taking into account geographical factors, GERPM aims to improve prediction accuracy and provide robust predictions for urban residential rents. The model is evaluated using housing data from Nanjing, a major city in China, and compared with representative individual prediction models, the equal weight combination model, and the ensemble learning model. The experimental results demonstrate that GERPM outperforms other models in terms of prediction performance. Furthermore, the model’s effectiveness and robustness are validated by applying it to other major cities in China, such as Shanghai and Hangzhou. Overall, GERPM shows promising potential in accurately predicting urban residential rents and contributing to the advancement of the rental market.

BackgroundIdentifying a proper model structure, using methods that address both structural and parameter uncertainty, is a crucial problem within the systems approach to biology. And yet, it has a marginal presence in the recent literature. While many existing approaches integrate methods for simulation and parameter estimation of a single model to address parameter uncertainty, only few of them address structural uncertainty at the same time. The methods for handling structure uncertainty often oversimplify the problem by allowing the human modeler to explicitly enumerate a relatively small number of alternative model structures. On the other hand, process-based modeling methods provide flexible modular formalisms for specifying large classes of plausible model structures, but their scope is limited to deterministic models. Here, we aim at extending the scope of process-based modeling methods to inductively learn stochastic models from knowledge and data.ResultsWe combine the flexibility of process-based modeling in terms of addressing structural uncertainty with the benefits of stochastic modeling. The proposed method combines search trough the space of plausible model structures, the parsimony principle and parameter estimation to identify a model with optimal structure and parameters. We illustrate the utility of the proposed method on four stochastic modeling tasks in two domains: gene regulatory networks and epidemiology. Within the first domain, using synthetically generated data, the method successfully recovers the structure and parameters of known regulatory networks from simulations. In the epidemiology domain, the method successfully reconstructs previously established models of epidemic outbreaks from real, sparse and noisy measurement data.ConclusionsThe method represents a unified approach to modeling dynamical systems that allows for flexible formalization of the space of candidate model structures, deterministic and stochastic interpretation of model dynamics, and automated induction of model structure and parameters from data. The method is able to reconstruct models of dynamical systems from synthetic and real data.

Leveraging advanced ensemble models to increase building energy performance prediction accuracy in the residential building sector

Background: Feature selection techniques are important factors for improving machine learning models because they increase prediction accuracy and decrease the time to create a model. Recently, feature selection techniques have been employed on software quality prediction problems with different results and no clear indication of which techniques are frequently used.Objective: This study aims to conduct a systematic review of the application of feature selection techniques in software quality prediction and answers eight research questions.Method: The review evaluates 15 papers in 9 journals and 6 conference proceedings from 2007 to 2017 using the standard systematic literature review method.Results: The results obtained from this study reveal that the filter feature selection method was the most commonly used in the studies (60%) and RELIEF was the most employed among this method, and a limited number of studies employed an ensemble method. Several studies used public datasets available in the PROMISE software project repository (60%). Most studies focused on software defect prediction (classification problem) using area under curve (AUC) as a primary evaluation measure, whereas only two studies focused on software maintainability prediction (regression problem) using mean magnitude of relative error (MMRE) as a primary evaluation measure. All selected studies performed k-fold cross-validation to evaluate model accuracy. Individual prediction models were mostly employed and ensemble models appeared only in three studies. Naive Bayes was the most investigated among individual models, whereas Random forest was the most investigated among ensemble models.Conclusion: Feature selection techniques used by selected primary studies have a positive impact on the performance of the prediction models. Further, both ensemble feature selection method and ensemble models have the ability for increasing prediction accuracy over single methods or individual models and have reported improvement in the prediction accuracy; however, the application of these techniques in software quality prediction is still limited.

Sub-Saharan Africa continues to shoulder the heaviest burden of malaria. The 2024 World Health Organisation (WHO) malaria report highlighted that Africa contributed an alarming 94% of the global cases and 95% of the deaths. In the WHO African region, progress towards elimination and management of malaria is hindered by weak health systems and a lack of traditional diagnostic methods such as microscopy and malaria Rapid Diagnostic Tests (mRDTs). The primary aim of this paper is to develop a machine learning (ML) ensemble model for malaria diagnosis using clinical and demographic data, tailored for resource-limited settings. A retrospective study was conducted using 637 patient records from Gutu Mission Hospital and Gweru Provincial Hospital in Zimbabwe. Clinical symptoms (fever, chills, abdominal pain, headache and diarrhea) and demographic features (age, gender, residence and travel history) were analysed. Data preprocessing included handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) and feature selection using Recursive Feature Elimination (RFE). Seven individual ML models, including Logistic regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boosting (GB), K-Nearest Neighbour (KNN), Naive Bayes (NB) and XGBoost were trained and evaluated on the malaria dataset. The individual models were further combined to build, train and evaluate ensemble models such as Bagging, Stacking, Soft Voting and AdaBoost. Model performance was assessed using accuracy, precision, confusion matrices, recall, F1 score, and AUC-ROC metrics. Clinical symptoms (chills: p-value=0.001, fever: p=0.003, diarrhea: p=0.01, abdominal pain: p< 0.001) were statistically significant predictors of malaria. Of the demographic factors, only travel history (p=0.02) showed a significant association with malaria. Among the seven individual ML models, GB achieved the highest predictive performance (Accuracy =0.94), followed by RF (Accuracy =0.94) and XGBoost (Accuracy =0.93). The stacking ensemble model outperformed all individual ML models and other ensemble models (bagging, soft voting and AdaBoost), achieving accuracy =0.96, precision =0.95, recall =0.98, F1 score =0.96 and AUC-ROC =0.98. This study demonstrates that ML, particularly ensemble models, can be used to improve malaria diagnosis significantly. The integration of these models into a web-based application could provide a scalable and accessible diagnostic tool for healthcare workers in resource-limited settings.

BackgroundAccurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation.MethodsThis 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h.ResultsIn total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71–0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66–0.71)) (z score of − 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible.ConclusionsWhile both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h.Trial registrationClinicalTrials.gov, NCT02518646. Registered on 21 July 2015.

Background and objectiveThe increasing global prevalence of diabetes has led to a surge in complications, significantly burdening healthcare systems and affecting patient quality of life. Early prediction of these complications is critical for timely intervention, yet existing models often rely heavily on clinical indicators while underutilizing fundamental laboratory test parameters. This study aims to bridge this gap by leveraging the 12 most frequently tested laboratory indicators in diabetic patients to develop an optimized predictive model for diabetes complications.MethodsA comprehensive dataset was established through meticulous data collection from a high-volume tertiary hospital, followed by extensive data cleaning and classification. Various machine learning classifiers, including Random Forest, XGBoost, Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained on this dataset to evaluate their predictive performance. We further introduced an ensemble learning model with Bayesian optimization to enhance accuracy and cost-efficiency. Additionally, feature importance analysis was conducted to refine the model by reducing testing costs while maintaining high predictive accuracy.ResultsOur ensemble model with Bayesian optimization demonstrated superior performance, achieving over 90% accuracy in predicting various diabetic complications, with an outstanding 98.50% accuracy and 99.76% AUC for diabetic nephropathy. Feature correlation analysis enabled a refined model that not only improved predictive accuracy but also reduced overall medical costs by 2.5% through strategic feature elimination.ConclusionsThis study makes three key contributions: (1) Development of a high-quality dataset based on fundamental laboratory indicators, (2) Creation of a highly accurate predictive model using ensemble learning and Bayesian optimization, particularly excelling in diabetic nephropathy prediction, and (3) Implementation of a cost-efficient diagnostic approach that reduces testing expenses without compromising accuracy. The proposed model provides a strong foundation for future research and practical clinical applications, demonstrating the potential of integrating machine learning with cost-conscious medical testing.

Identification of the sensitive regions to soil erosion can be the foremost step in controlling the vigorous pace of soil degradation in agricultural lands. This study employs individual and ensemble models including compound factor (CF), analytical hierarchy process (AHP), and technique for order preference by similarity to ideal solution-analytical hierarchy process (TOPSIS-AHP) for identifying the regions susceptible to soil erosion in Dnyanganga watershed of India using morphometric attributes (MAs). Thirteen MAs were computed using the newly released TanDEM-X data, which is a bi-static X-band interferometric Synthetic Aperture Radar (SAR) acquisition. Weights for each of the MAs were calculated using AHP, which was employed in both AHP and TOPSIS-AHP models. The outcome of the AHP model displays that watershed slope (Sw), stream frequency (Fu), ruggedness number (Rn), and relative relief (Rr) greatly influences soil erosion in the study area. The priority ranking outcome of individual and ensemble models are compared using the Spearman correlation coefficient test (SCCT) and Kendall tau correlation coefficient test (KTCCT) to select the best model for soil erosion susceptibility mapping. The result of the non-parametric tests reveals that TOPSIS-AHP ensemble model is better in ranking alternatives than the individual models. According to TOPSIS-AHP ensemble model, the sub-watershed DNY16 is highly susceptibility to erosion, whereas DNY1 is least susceptible. Based on the ensemble model, 14.54%, 41.02%, and 44.43% of the area in Dnyanganga watershed fall under high, medium and low soil erosion susceptible classes, respectively. The results from this study will aid decision makers in better conservation planning of soil and water resources.

Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction

Introduction: Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, aimed at improving early detection and survival rates. However, there has been no comprehensive review and meta-analysis of model performance across different sociocultural contexts. Therefore, this review aims to systematically examine the performance of lung cancer risk prediction models in Western and Asian populations. Methods: PubMed and EMBASE were searched from inception to January 2023. Studies published in English and proposed a validated model on human populations with well-defined predictive performances were included. Two reviewers independently screened titles and abstracts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to appraise study quality. A random-effects meta-analysis was performed and a 95% confidence interval (CI) for model performance was provided. Between-study heterogeneity was adjusted for using the Hartung-Knapp-Sidik-Honkman test. Results: Fifty-four studies were included, with 42 from Western and 12 from Asian countries. Most Western studies were designed for ever-smokers (19/42; 45.2%) and the general population (17/42; 40.5%), and only two Asian studies developed models exclusively for never-smokers. Across both Western and Asian prediction models, the three most consistently included risk factors are age, sex, and family cancer history. In 45.2% (19/42) of Western and 50.0% (6/12) of Asian studies, models incorporated both traditional risk factors and biomarkers; 14.8% (8/54) directly compared their biomarker-based models with those incorporating only traditional risk factors, demonstrating better discrimination. Machine-learning algorithms were applied in eight Western models and two Asian models. External validation of PLCOM2012 (AUC=0.748; 95% CI: 0.719-0.777) outperformed prediction models such as Bach (AUC=0.710; 95% CI: 0.674-0.745) and Spitz models (AUC=0.698; 95% CI: 0.640-0.755). Conclusion: Despite showing promising results, the majority of Asian risk models in our study lack external validation. Our review also reveals a significant gap in prediction models for never-smokers. Future research should externally validate existing Asian models or incorporate relevant Asian risk factors in widely-used Western models (PLCOM2012) to account for their unique risk profiles and lung cancer progression patterns. Citation Format: Yah Ru Juang, Lina Ang, Wei Jie Seow. The predictive performance of risk prediction models in determining lung cancer incidence in Western and Asian countries: A systematic review and meta-analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6210.

BackgroundEmergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding.ObjectiveThe aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding.MethodsWe searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies.ResultsPotential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments.ConclusionsThere is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

Summary1. The predictive modelling approach to bioassessment estimates the macroinvertebrate assemblage expected at a stream site if it were in a minimally disturbed reference condition. The difference between expected and observed assemblages then measures the departure of the site from reference condition.2. Most predictive models employ site classification, followed by discriminant function (DF) modelling, to predict the expected assemblage from a suite of environmental variables. Stepwise DF analysis is normally used to choose a single subset of DF predictor variables with a high accuracy for classifying sites. An alternative is to screen all possible combinations of predictor variables, in order to identify several ‘best’ subsets that yield good overall performance of the predictive model.3. We applied best‐subsets DF analysis to assemblage and environmental data from 199 reference sites in Oregon, U.S.A. Two sets of 66 best DF models containing between one and 14 predictor variables (that is, having model orders from one to 14) were developed, for five‐group and 11‐group site classifications.4. Resubstitution classification accuracy of the DF models increased consistently with model order, but cross‐validated classification accuracy did not improve beyond seventh or eighth‐order models, suggesting that the larger models were overfitted.5. Overall predictive model performance at model training sites, measured by the root‐mean‐squared error of the observed/expected species richness ratio, also improved steadily with DF model order. But high‐order DF models usually performed poorly at an independent set of validation sites, another sign of model overfitting.6. Models selected by stepwise DF analysis showed evidence of overfitting and were outperformed by several of the best‐subsets models.7. The group separation strength of a DF model, as measured by Wilks’Λ, was more strongly correlated with overall predictive model performance at training sites than was DF classification accuracy.8. Our results suggest improved strategies for developing reliable, parsimonious predictive models. We emphasise the value of independent validation data for obtaining a realistic picture of model performance. We also recommend assessing not just one or two, but several, candidate models based on their overall performance as well as the performance of their DF component.9. We provide links to our free software for stepwise and best‐subsets DF analysis.

A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging