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In today’s dynamic business world, organizations are increasingly relying on innovative technologies to improve the efficiency and effectiveness of their human resource (HR) management. Our study uses historical time and attendance data collected with the MojeUre time and attendance system to predict employee absenteeism, including sick and vacation leave, using machine learning methods. We integrate employee demographic data and the absence profiles on timesheets showing daily attendance patterns as fundamental elements for our analysis. We also convert the absence data into a feature-based format suitable for the machine learning methods used. Our primary goal in this paper is to evaluate how well we can predict sick leave and vacation leave over short- and long-term intervals using tree-based machine learning methods based on the predictive clustering paradigm. This paper compares the effectiveness of these methods in different learning settings and discusses their impact on improving HR decision-making processes.

The aim of this research is to shed light on the complex interactions between player workload, traits, match-related factors, football performance, and injuries in the English Premier League. Using a range of statistical and machine learning techniques, this study analyzed a comprehensive dataset that included variables such as player workload, personal traits, and match statistics. The dataset comprises information on 532 players across 20 football clubs for the 2020–2021 English Premier League season. Key findings suggest that data, age, average minutes played per game, and club affiliations are significant indicators of both performance and injury incidence. The most effective model for predicting performance was Ridge Regression, whereas Extreme Gradient Boosting (XGBoost) was superior for predicting injuries. These insights are invaluable for data-driven decision-making in sports science and football teams, aiding in injury prevention and performance enhancement. The study’s methodology and results have broad applications, extending beyond football to impact other areas of sports analytics and contributing to a flexible framework designed to enhance individual performance and fitness.

In this study, we evaluate predictive modelling techniques within project management, employing diverse architectures such as the LSTM, CNN, CNN-LSTM, GRU, MLP, and RNN models. The primary focus is on assessing the precision and consistency of predictions for crucial project parameters, including completion time, required personnel, and estimated costs. Our analysis utilises a comprehensive dataset that encapsulates the complexities inherent in real-world projects, providing a robust basis for evaluating model performance. The findings, presented through detailed tables and comparative charts, underscore the collective success of the models. The LSTM model stands out for its exceptional performance in consistently predicting completion time, personnel requirements, and estimated costs. Quantitative evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), corroborate the efficacy of the models. This study offers insights into the success observed, reflecting the potential for further refinement and continuous exploration to enhance the accuracy of predictive models in the ever-evolving landscape of project management.

The extensive use of polypropylene (PP) in various industries has heightened interest in developing efficient methods for recycling and optimising its mixtures. This study focuses on formulating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The investigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, attaining R2 values over 0.99. Symbolic regression further enhanced these models, offering significant improvements. To mitigate overfitting, empirical noise and variable swapping were introduced, increasing the models’ robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the development of recycling technologies and sustainable practices in the polymer industry by optimising processing and enhancing the use of recycled materials.

Introduction: Health risk assessment (HRA) strategies are cornerstone for health systems transformation toward value-based patient-centred care. However, steps for HRA adoption are undefined. This article analyses the process of transference of the Adjusted Morbidity Groups (AMG) algorithm from the Catalan Good Practice to the Marche region (IT) and to Viljandi Hospital (EE), within the JADECARE initiative (2020–2023). Description: The implementation research approach involved a twelve-month pre-implementation period to assess feasibility and define the local action plans, followed by a sixteen-month implementation phase. During the two periods, a well-defined combination of experience-based co-design and quality improvement methodologies were applied. Discussion: The evolution of the Catalan HRA strategy (2010–2023) illustrates its potential for health systems transformation, as well as its transferability. The main barriers and facilitators for HRA adoption were identified. The report proposes a set of key steps to facilitate site customized deployment of HRA contributing to define a roadmap to foster large-scale adoption across Europe. Conclusions: Successful adoption of the AMG algorithm was achieved in the two sites confirming transferability. Marche identified the key requirements for a population-based HRA strategy, whereas Viljandi Hospital proved its potential for clinical use paving the way toward value-based healthcare strategies.

The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear. One approach would be to use the baseline gland volume (BGV) as a denominator in all calculations throughout AS (nonadaptive PSAD, PSADNA), while another would be to remeasure gland volume at each new magnetic resonance imaging scan (adaptive PSAD, PSADA). In addition, little is known about the predictive value of serial PSAD in comparison to PSA. We applied a long short-term memory recurrent neural network to an AS cohort of 332 patients and found that serial PSADNA significantly outperformed both PSADA and PSA for follow-up prediction of PCa progression because of its high sensitivity. Importantly, while PSADNA was superior in patients with smaller glands (BGV ≤55 ml), serial PSA was better in men with larger prostates of >55 ml. Patient summary: Repeat measurements of prostate-specific antigen (PSA) and PSA density (PSAD) are the mainstay of active surveillance in prostate cancer. Our study suggests that in patients with a prostate gland of 55 ml or smaller, PSAD measurements are a better predictor of tumour progression, whereas men with a larger gland may benefit more from PSA monitoring.

Background: Artificial intelligence (AI) has the potential to revolutionise paramedic education. As well as allowing for personalised learning experiences tailored to individual needs and learning styles, it can provide simulations, intelligent tutoring systems, automated grading and assessment, and predictive analytics. Objective: To investigate the role of artificial intelligence in transforming the landscape of paramedic education and evaluate its potential to improve learning outcomes. Methods: This review presented the role of AI in paramedic education and its perspective over the past twenty years. It included high-quality data and comprehensive investigations of articles available in renowned databases. Results: AI-based training and simulation technologies, such as virtual patients, surgical simulators, and intelligent tutoring systems,are increasingly being used in paramedic education. Virtual patients use computer-generated avatars to display symptoms and react to therapies, while surgical simulators use accurate anatomical models and haptic feedback devices to simulate surgical operations. Conclusion: AI has the potential to fundamentally alter how students learn, the kind of education they receive, and the efficiency with which healthcare is delivered. It can create immersive training environments, analyse medical data, and help students feel more competent, confident, and capable. This potential can be harnessed to enhance paramedic education and improve patient care outcomes.

Aim: Athletic pre-season testing can establish functional baseline for comparison following concussion. Whether impacts of future concussions may be foretold by such testing is little known. Materials & methods: Two sets of models for a significant burden of concussion were generated: a traditional approach using a series of logistic regressions, and a penalized regression approach using elastic net. Results: 3091 youth and adult athletes were baseline-assessed. 90 subsequently experienced concussion and 35 were still experiencing a significant burden of concussion when tested within two weeks. Both models associated prior history of head injury and visual attention-related metrics with a significant burden of concussion. Conclusion: Pre-season testing of visual attention may identify athletes who are at risk for significant sports-related concussion.

AbstractThe development of cost overrun prediction models using data mining techniques has considerably increased in recent years. Estimating the final cost of construction projects is essential during the contract award stage of the building process. Projects variables from archival data are important in developing prediction models. This research examines the effectiveness of support vector machines in predicting construction project cost overruns using data from archival records.The independent variables, like number of stories, gross floor area, change in scope, contract type, provisional sum, tendering type, and initial contract sum, were extracted from historical records. In this study, SVM models using linear, RBF, and polynomial kernel functions demonstrated that SVM using linear and polynomial kernel techniques were used in this research. This study looks at how well data mining tools forecast cost overruns in building projects using information from historical records.The results revealed that the linear kernel SVM model could produce accurate construction cost predictions with 0.99 R2, 0.099 RMSE, 0.05 MAE, 0.278 MAPE, and 0.01 MSE on the accuracy test data. When considered collectively, it is clear that gross floor space, story count, tendering method, and scope modification are reliable indicators of cost overruns in the construction sector.The created SVM model can be applied as a cost-estimating tool to predict potential cost overruns for Ghanaian construction projects.

Background: Respiratory syncytial virus (RSV) is a leading cause of childhood mortality in infants below 6 months of age. In low-income and middle-income countries (LMICs), the public health burden is substantial and resources are limited. It is critical to inform decision makers about effectiveness of new interventions. Methods: We developed a mathematical model where individual RSV subtype A (RSV-A) and B (RSV-B) maternally derived neutralizing titers were predicted at time of birth after maternal vaccination with the RSV prefusion F protein-based vaccine. We estimated the subsequent duration of vaccine-induced immunity and compared this to the age at time of death distribution in the RSV GOLD Mortality Database to predict the potential impact of maternal vaccination on RSV-related childhood mortality. We used country-specific timing of antenatal care visits distributions and mortality estimates to make country-specific predictions for number of cases averted. Findings: The model predicts that on average a neonate born at 40 weeks gestational age will be protected between 6 and 7 months from RSV-A and approximately 5 months from RSV-B related mortality. We estimated the potential impact of RSV-related mortality for in-hospital and out-of-hospital cases in LMICs and predicted that in 51 GAVI-eligible countries maternal vaccination could avert between 55% and 63% of the RSV-related in-hospital mortality cases below 6 months of age. Interpretation: We show that maternal vaccination could substantially decrease RSV-A and RSV-B related in-hospital and out-of-hospital mortality in LMICs in the first 6 months of life.

Background: Geostatistical analysis of health data is increasingly used to model spatial variation in malaria prevalence, burden, and other metrics. Traditional inference methods for geostatistical modelling are notoriously computationally intensive, motivating the development of newer, approximate methods for geostatistical analysis or, more broadly, computational modelling of spatial processes. The appeal of faster methods is particularly great as the size of the region and number of spatial locations being modelled increases. Methods: This work presents an applied comparison of four proposed 'fast' computational methods for spatial modelling and the software provided to implement them—Integrated Nested Laplace Approximation (INLA), tree boosting with Gaussian processes and mixed effect models (GPBoost), Fixed Rank Kriging (FRK) and Spatial Random Forests (SpRF). The four methods are illustrated by estimating malaria prevalence on two different spatial scales—country and continent. The performance of the four methods is compared on these data in terms of accuracy, computation time, and ease of implementation. Results: Two of these methods—SpRF and GPBoost—do not scale well as the data size increases, and so are likely to be infeasible for larger-scale analysis problems. The two remaining methods—INLA and FRK—do scale well computationally, however the resulting model fits are very sensitive to the user's modelling assumptions and parameter choices. The binomial observation distribution commonly used for disease prevalence mapping with INLA fails to account for small-scale overdispersion present in the malaria prevalence data, which can lead to poor predictions. Selection of an appropriate alternative such as the Beta-binomial distribution is required to produce a reliable model fit. The small-scale random effect term in FRK overcomes this pitfall, but FRK model estimates are very reliant on providing a sufficient number and appropriate configuration of basis functions. Unfortunately the computation time for FRK increases rapidly with increasing basis resolution. Conclusions: INLA and FRK both enable scalable geostatistical modelling of malaria prevalence data. However care must be taken when using both methods to assess the fit of the model to data and plausibility of predictions, in order to select appropriate model assumptions and parameters. [ABSTRACT FROM AUTHOR]

This work proposes a data mining-based prediction and development of the water quality index in mining areas. A mathematical equation for the index and predicted model is derived quantitatively in the study. Predicting water quality often involves applying conventional data mining techniques like classification and regression. Predictive learning and testing models can be evaluated using previous monitoring in real-time datasets and implementing k-fold cross-validation methods. The "decision trees" classification methodology outperforms other classification methods with 97.30% and 99.50% accuracy for training and testing model validation. MAE, RMSE, MSE, and R-squared are used in regression analysis to evaluate prediction accuracy and model performance. Regression model errors are absent with an R-squared value of 1. The present research showcases the efficacy of data mining techniques in accurately estimating mine water quality. These findings help improve mine water quality management. [ABSTRACT FROM AUTHOR]

The driving cycle is a series of driving behaviours, such as acceleration, braking, and cruising, that occur over a set length of time. Predicting the driving cycle can help to improve vehicle performance or anticipate the range of an electric car. Based on prior data, long short-term memory (LSTM) networks can be used to forecast a vehicle's driving cycle. This paper studies a driving cycle prediction based on LSTM by recurrent neural network (RNN) using developed driving cycle data. The objectives of this paper are; to develop an Ipoh driving cycle (IDC), to develop a prediction of future IDC and to analyze the prediction of IDC. Firstly, the driving data is collected in three different routes in Ipoh city at back-from-work times. Then the data is divided into micro-trips and the driving features are extracted. The features are used to develop a driving cycle using k-means clustering approach. The prediction is developed after the training of neural networks by using LSTM network approach with root mean square error (RSME) of 6.2252%. [ABSTRACT FROM AUTHOR]

The current research focuses on the chemical evaluation of groundwater in the southern region to the Jabalpur city that is utilized for drinking and is based on the Water Quality Index. To conduct the groundwater quality analysis and subsequently calculate the Water Quality Index, water samples from 37 wells were collected within the research area. Twelve different water characteristics were taken into account, including pH, Total Hardness, Total Dissolved Solids, Calcium, Magnesium, Sodium, Potassium, Iron, Fluoride, Bicarbonate, Sulphate, and Chloride. The distribution of the groundwater samples, as determined by the computed WQI, was found to be 0.00% in the excellent category, 0.00% in the Good category, 86.00% in the Poor category, 14% in the Very Poor category, 0.00% in the unsuitable category, respectively. Using IBM® SPSS® Statistics 16 software, an artificial neural network model (ANNM) was created to forecast changes in the groundwater water quality index (WQI). Sum of squares error functions and a coefficient of determination are used to assess the ANNM for both training and testing samples (R2). The findings demonstrated that the ANNM was highly effective in making predictions, with sum of squares errors for the training and testing samples of 0.048 and 0.033, respectively, and an R2 value of 0.995. It is also shown that the factors HCO3, Fluoride, and Total Hardness have a significant influence on model prediction. [ABSTRACT FROM AUTHOR]

Background: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. Methods: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). Results: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002–2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. Conclusions: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping. [ABSTRACT FROM AUTHOR]

Predictive modelling is fundamental to ecology and essential for objective biodiversity assessment. However, while predictive biodiversity models are generally well‐developed, models for predicting patterns within (e.g. composition, structure) and among ecosystems (e.g. ecosystem identity, spatial properties) have not been adequately operationalized. We contend the scarcity of such models marks a concerning gap in the scientific community's ability to make ecosystem predictions across landscapes, and more broadly for supporting the conservation of biodiversity and ecosystem functions. We propose ecosystem spatial pattern models (ESPM) to fill this gap in modelling capacity. Under our approach to ESPM, spatial patterns of ecosystem properties are the basis for resolving ecosystem organization at local and landscape extents. Our integrative modelling framework differs from others in that it accords biotic and abiotic constituents equally, consistent with their joint mechanistic influence on ecosystem establishment and dynamics. Development of ESPM is especially timely for ecosystem assessment is undergoing a groundswell, as scientists and conservation groups propose ambitious targets for ecosystem conservation and restoration. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Can machine learning predict the number of oocytes retrieved from controlled ovarian hyperstimulation (COH)? SUMMARY ANSWER Three machine-learning models were successfully trained to predict the number of oocytes retrieved from COH. WHAT IS KNOWN ALREADY A number of previous studies have identified and built predictive models on factors that influence the number of oocytes retrieved during COH. Many of these studies are, however, limited in the fact that they only consider a small number of variables in isolation. STUDY DESIGN, SIZE, DURATION This study was a retrospective analysis of a dataset of 11,286 cycles performed at a single centre in France between 2009 and 2020 with the aim of building a predictive model for the number of oocytes retrieved from ovarian stimulation. The analysis was carried out by a data analysis team external to the centre using the Substra framework. The Substra framework enabled the data analysis team to send computer code to run securely on the centre's on-premises server. In this way, a high level of data security was achieved as the data analysis team did not have direct access to the data, nor did the data leave the centre at any point during the study. PARTICIPANTS/MATERIALS, SETTING, METHODS The Light Gradient Boosting Machine algorithm was used to produce three predictive models: one that directly predicted the number of oocytes retrieved and two that predicted which of a set of bins provided by two clinicians the number of oocytes retrieved fell into. The resulting models were evaluated on a held-out test set and compared to linear and logistic regression baselines. In addition, the models themselves were analysed to identify the parameters that had the biggest impact on their predictions. MAIN RESULTS AND THE ROLE OF CHANCE On average, the model that directly predicted the number of oocytes retrieved deviated from the ground truth by 4.21 oocytes. The model that predicted the first clinician's bins deviated by 0.73 bins whereas the model for the second clinician deviated by 0.62 bins. For all models, performance was best within the first and third quartiles of the target variable, with the model underpredicting extreme values of the target variable (no oocytes and large numbers of oocytes retrieved). Nevertheless, the erroneous predictions made for these extreme cases were still within the vicinity of the true value. Overall, all three models agreed on the importance of each feature which was estimated using Shapley Additive Explanation (SHAP) values. The feature with the highest mean absolute SHAP value (and thus the highest importance) was the antral follicle count, followed by basal AMH and FSH. Of the other hormonal features, basal TSH, LH, and testosterone levels were similarly important and baseline LH was the least important. The treatment characteristic with the highest SHAP value was the initial dose of gonadotropins. LIMITATIONS, REASONS FOR CAUTION The models produced in this study were trained on a cohort from a single centre. They should thus not be used in clinical practice until trained and evaluated on a larger cohort more representative of the general population. WIDER IMPLICATIONS OF FINDINGS These predictive models for the number of oocytes retrieved from COH may be useful in clinical practice, assisting clinicians in optimizing COH protocols for individual patients. Our work also demonstrates the promise of using the Substra framework for allowing external researchers to provide clinically relevant insights on sensitive fertility data in a fully secure, trustworthy manner and opens a number of exciting avenues for accelerating future research. STUDY FUNDING/COMPETING INTEREST(S) This study was funded by the French Public Bank of Investment as part of the Healthchain Consortium. T.Fe. C.He. J.C. C.J. C.-A.P. and C.Hi. are employed by Apricity. C.Hi. has received consulting fees and honoraria from Vitrolife, Merck Serono, Ferring, Cooper Surgical, Dibimed, Apricity, and Fairtility and travel support from Fairtility and Vitrolife, participates on an advisory board for Merck Serono, was the founder and organizer of the AI Fertility conference, has stock in Aria Fertility, TMRW, Fairtility, Apricity, and IVF Professionals, and received free equipment from Planar in exchange for first user feedback. C.J. has received a grant from BPI. J.C. has also received a grant from BPI, is a member of the Merck AI advisory board, and is a board member of Labelia Labs. C.He has a contract for medical writing of this manuscript by CHU Nantes and has received travel support from Apricity. A.R. haș received honoraria from Ferring and Organon. T.Fe. has received a grant from BPI. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

Keywords: vacancy statistics; online data; time series; predictive modelling; unemployment; employment EN vacancy statistics online data time series predictive modelling unemployment employment 1630 1649 20 09/26/23 20231001 NES 231001 We explore data collected as an administrative by-product of an online job advertisement portal with dominant market coverage in Slovakia. While the potential of other online data, such as trends in internet searches or social networks, in predicting unemployment was explored in the past (e.g. [3]; [10]; [11]; [21]; [44]), we are not aware of any study exploring trends in the total number of OJVs specifically to predict unemployment or employment. Data We aim to assess the ability of OJV postings to capture and predict the development of official job vacancy, employment and unemployment statistics, which we jointly refer to as key labour market indicators. From the perspective of labour market analysis, OJV data present an even richer source of information than data acquired from online search (e.g. Google Trends) or social networks (e.g. Twitter) because they document a substantial share of the hiring process. [Extracted from the article]

Existing literature on optimizing inventory levels in pharmaceutical supply chains has focused on a limited set of drivers. However, the global supply chain disruptions produced by the Covid-19 pandemic demonstrated the need for a more nuanced picture of the inventory management drivers in this sector to identify profitable inventory configurations while fulfilling demands and safety margins. To address this gap in the literature, this paper identifies key drivers impacting inventory levels and develops a framework for assessing inventory configurations in pharmaceutical supply chains. The framework is tested using a single case study approach. The case study showed that while external and downstream supply chain factors were recognized as being critical to pursuing inventory reduction initiatives, internal factors prevailed when making inventory management decisions. The framework developed in this paper may assist practitioners in identifying the most important factors impacting inventory levels within a specific pharmaceutical supply chain configuration and is in use in the industry today. [ABSTRACT FROM AUTHOR]

This paper explores the emergence of insurtech and its impact on general insurance product innovation. Insurtech, a term that refers to the application of technology in the insurance industry, has disrupted traditional insurance models and brought about significant changes in product development, distribution, and customer experience. Through a comprehensive analysis of the insurtech landscape, this paper examines various technological advancements and their influence on general insurance products. It also discusses the challenges and opportunities faced by insurance companies as they embrace insurtech and work towards creating innovative products. By presenting case studies and examples, this paper aims to provide insights into the future direction of general insurance product innovation in the era of insurtech. [ABSTRACT FROM AUTHOR]

Colorectal cancer persistently ranks among the top causes of cancer-related mortality globally. The development of superior predictive methodologies is imperative for augmenting survival outcomes. This systematic review, conducted in accordance with PRISMA-P guidelines, scrutinizes studies carried out between 2013 and 2023 that apply machine learning models to prognosticate survival in colorectal cancer patients, particularly those models incorporating clinical data and gene expression profiles. Criteria for inclusion comprised studies employing machine learning techniques, with specific emphasis on those integrating clinical data and gene expression profiles for predictive purposes. Studies devoid of explicit methodological delineation or not written in English were excluded. Decision trees, neural networks, and support vector machines emerged as the most frequently scrutinized models in the review. While some models manifested high accuracy, others underscored areas requiring refinement. Predominant data sources included patient clinical records, gene expression datasets, and molecular profiling. The results underscore the potential of machine learning in bolstering predictive precision, thereby implicating a trajectory for future research targeting the optimization of patient prognosis and treatment outcomes in colorectal cancer. [ABSTRACT FROM AUTHOR]

Predictive spatial analysis in archaeology refers to the use of geostatistical and computational techniques to assess the relationships between the distribution of archaeological sites and the environmental context. It is increasingly becoming an essential tool in scientific-academic research and rescue archaeology. In the interconnection between these two working contexts, this study aims to explore the advantages of applying an inductive approach, combining Geographical Information Systems (GIS) and geostatistical methods, to identify areas with different degrees of probability for the preservation of the Palaeolithic record in the lower valley of the Lis River (Leiria region, central Portugal). Topographic, hydrographic, geological, and geomorphological data were collected, processed, and integrated into a spatial database, using a GIS, to define the environmental conditioning factors. After applying a spatial multicollinearity procedure, slope, topographical position index, cost-weighted distance to access the watercourses, heat load index and lithology were chosen as input variables for the predictive modelling. Archaeological data, corresponding to the location of known Palaeolithic sites, were also retrieved from the official records. Three models were implemented using regression and multi-criteria methods; for Model 1 a Generalised Additive Model (GAM) was applied, while for Model 2 and Model 3 two distinct combinations of Probabilistic Frequency Ratio (PFR) and Analytic Hierarchy Process (AHP) were used. The spatial cross-tabulation between the obtained predictive maps and the location of the known Palaeolithic sites shows that all the models are acceptable. However, the combinations of PFR + AHP seem to evidence greater efficacy compared to the GAM. Model 3 offers the best performance (90%), followed by Model 2 (87%) and Model 1 (68%). Possible sources of inaccuracies in the input data and the spatial modelling results were critically discussed. Model 3 generated the most robust and reliable predictive prototype map. The maps produced can serve local and regional agencies as a complement and aid decision-making in public policies for spatial planning, and in the conservation/management of cultural heritage. These cartographic representations can provide quicker identification of areas of interest, for example, in the context of rescue archaeology, along with a substantial reduction in time and costs for project promoters, as well as helping in the environmental impact assessments. [ABSTRACT FROM AUTHOR]

Background: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. Design: Systematic review and meta-analyses. Participants: Older adults (≥ 65 years) in any setting. Intervention: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months. Outcome measures: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. Results: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 – 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 – 0.86) for mortality over 6 months, signifying good discriminatory power. Conclusion: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes. [ABSTRACT FROM AUTHOR]

Aim Materials and Methods Results Conclusions To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self‐report questionnaires and demographic data.Self‐reported measures of periodontitis, demographic data and clinically established Stage III/IV periodontitis status were extracted from two Danish population‐based cohorts (The Copenhagen Aging and Midlife Biobank [CAMB] and The Danish Health Examination Survey [DANHES]) and used to develop cross‐validated machine learning models for the prediction of clinically established Stage III/IV periodontitis. Models were trained using 10‐fold cross‐validations repeated three times on the CAMB dataset (n = 1476), and the resulting models were validated in the DANHES dataset (n = 3585).The prevalence of Stage III/IV periodontitis was 23.2% (n = 342) in the CAMB dataset and 9.3% (n = 335) in the DANHES dataset. For the prediction of clinically established Stage III/IV periodontitis in the CAMB cohort, models reached area under the receiver operating characteristics (AUROCs) of 0.67–0.69, sensitivities of 0.58–0.64 and specificities of 0.71–0.80. In the DANHES cohort, models derived from the CAMB cohort achieved AUROCs of 0.64–0.70, sensitivities of 0.44–0.63 and specificities of 0.75–0.84.Applying cross‐validated machine learning algorithms to demographic data and self‐reported measures of periodontitis resulted in models with modest capabilities for the prediction of Stage III/IV periodontitis in two Danish cohorts. [ABSTRACT FROM AUTHOR]

Deep eutectic solvents may develop a pivotal role in future technologies considering sustainability and safety as pivotal aspects for chemistry developments. The possible application of these fluids for heat transfer operations is of great relevance for which the knowledge of thermal properties such as thermal conductivity is required as well as inferring structure–property relationships which allow reverse design of the fluids according to the technological requirements. Considering the technological relevance of this property, the available literature on the thermal conductivity for deep eutectic solvents is critically discussed showing strengths and weaknesses. The analysis of the state-of-the-art shows the future needs in this research field considering the application of these solvents for thermal-related technologies. The review indicates the scarcity of reliable experimental data and the need of predictive methods, which could be used for process design and solvent screening purposes. Likewise, considering the relevance of developing predictive methods for in silico design of these fluids according to industrial needs, the available predictive theoretical approaches are analysed showing their reliability as well as future needs. Finally, considering the need of developing suitable and reliable structure–property relationship, the molecular level basis of thermal conductivity in deep eutectic solvents is discussed, showing the role of hydrogen bonding and the effects rising from the involved hydrogen bond donors and acceptors as well as the eutectic compositions. This work reports the first literature review and analysis on thermal conductivity for deep eutectic solvents considering an experimental and theoretical approach as well as providing support for the molecular basis of this technologically relevant property, thus contributing to the development of environmentally friendly materials for thermal-related technologies. [ABSTRACT FROM AUTHOR]

Chronic hepatitis C (HCV) is a primary cause of hepatocellular carcinoma (HCC). Although antiviral treatment reduces risk of HCC, few studies quantify the impact of treatment on long‐term risk in the era of direct‐acting antivirals (DAA). Using data from the Chronic Hepatitis Cohort Study, we evaluated the impact of treatment type (DAA, interferon‐based [IFN], or none) and outcome (sustained virological response [SVR] or treatment failure [TF]) on risk of HCC. We then developed and validated a predictive risk model. 17186 HCV patients were followed until HCC, death or last follow‐up. We used extended landmark modelling, with time‐varying covariates and propensity score justification and generalized estimating equations with a link function for discrete time‐to‐event data. Death was considered a competing risk. We observed 586 HCC cases across 104,000 interval‐years of follow‐up. SVR from DAA or IFN‐based treatment reduced risk of HCC (aHR 0.13, 95% CI 0.08–0.20; and aHR 0.45, 95% CI 0.31–0.65); DAA SVR reduced risk more than IFN SVR (aHR 0.29, 95% CI 0.17–0.48). Independent of treatment, cirrhosis was the strongest risk factor for HCC (aHR 3.94, 95% CI 3.17–4.89 vs. no cirrhosis). Other risk factors included male sex, White race and genotype 3. Our six‐variable predictive model had 'excellent' accuracy (AUROC 0.94) in independent validation. Our novel landmark interval‐based model identified HCC risk factors across antiviral treatment status and interactions with cirrhosis. This model demonstrated excellent predictive accuracy in a large, racially diverse cohort of patients and could be adapted for 'real world' HCC monitoring. [ABSTRACT FROM AUTHOR]

Advance care planning (ACP) is generally considered as valuable in guiding treatments that are aligned with patients' preferences. Despite its benefits, there are some practical and legal difficulties in its implementation. Predictive modelling is increasingly used in clinical decision-making, for example, in predicting patients' life expectancy, thus enabling clinicians to initiate timely ACP conversations. This development could transform the way end-of-life conversations are implemented. In this article we advocate for the use of predictive modelling in assisting clinicians to initiate ACP conversations provided several safeguards are in place to address ethical concerns that arise. Predictive modelling applications resolve several practical and legal difficulties in conducting end-of-life conversations. Ethical concerns such as explicability, accountability, trustworthiness and reliability of these models in clinical settings are important considerations. However, safeguards are needed to address these ethical concerns to ensure the models are appropriately supportive of patient needs and interests. [ABSTRACT FROM AUTHOR]

Modelling claim frequency and claim severity are topics of great interest in property-casualty insurance for supporting underwriting, ratemaking, and reserving actuarial decisions. Standard Generalized Linear Models (GLM) frequency–severity models assume a linear relationship between a function of the response variable and the predictors, independence between the claim frequency and severity, and assign full credibility to the data. To overcome some of these restrictions, this paper investigates the predictive performance of Gradient Boosting with decision trees as base learners to model the claim frequency and the claim severity distributions of an auto insurance big dataset and compare it with that obtained using a standard GLM model. The out-of-sample performance measure results show that the predictive performance of the Gradient Boosting Model (GBM) is superior to the standard GLM model in the Poisson claim frequency model. Differently, in the claim severity model, the classical GLM outperformed the Gradient Boosting Model. The findings suggest that gradient boost models can capture the non-linear relation between the response variable and feature variables and their complex interactions and thus are a valuable tool for the insurer in feature engineering and the development of a data-driven approach to risk management and insurance. [ABSTRACT FROM AUTHOR]

Model-agnostic tools for the post-hoc interpretation of machine-learning models struggle to summarize the joint effects of strongly dependent features in high-dimensional feature spaces, which play an important role in semantic image classification, for example in remote sensing of landcover. This contribution proposes a novel approach that interprets machine-learning models through the lens of feature-space transformations. It can be used to enhance unconditional as well as conditional post-hoc diagnostic tools including partial-dependence plots, accumulated local effects (ALE) plots, permutation feature importance, or Shapley additive explanations (SHAP). While the approach can also be applied to nonlinear transformations, linear ones are particularly appealing, especially principal component analysis (PCA) and a proposed partial orthogonalization technique. Moreover, structured PCA and model diagnostics along user-defined synthetic features offer opportunities for representing domain knowledge. The new approach is implemented in the R package wiml, which can be combined with existing explainable machine-learning packages. A case study on remote-sensing landcover classification with 46 features is used to demonstrate the potential of the proposed approach for model interpretation by domain experts. It is most useful in situations where groups of feature are linearly dependent and PCA can provide meaningful multivariate data summaries. [ABSTRACT FROM AUTHOR]

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications. [ABSTRACT FROM AUTHOR]

Learning Analytics (LA) is increasingly used in Education to set prediction models from artificial intelligence to determine learning profiles. This study aims to determine to what extent K-nearest neighbor and random forest algorithms could become a useful tool for improving the teaching-learning process and reducing academic failure in two Physics courses at the Technological Institute of Monterrey, México (n = 268). A quasi-experimental and mixed method approach was conducted. The main results showed significant differences between the first and second term evaluations in the two groups. One of the main findings of the study is that the predictions were not very accurate for each student in the first term evaluation. However, the predictions became more accurate as the algorithm was fed with larger datasets from the second term evaluation. This result indicates how predictive algorithms based on decision trees, can offer a close approximation to the academic performance that will occur in the class, and this information could be use along with the personal impressions coming from the teacher.

This study employs a systematic and predictive modelling approach to investigate the structure and properties of multi-component borate glasses. In particular, this work is focused on understanding the individual and interaction effects of multiple constituents on several material properties. By leveraging advanced modeling techniques, this work examines how the inclusion and variation of B2O3, CaF2, TiO2, ZnO, and Na2CO3 influence the glass network, with particular attention to modifier fractions ≥ 30 mol%. This research addresses the gap in knowledge regarding the complex behavior of borate glasses in this high modifier fraction range, known as the borate anomaly, where prediction of glass structure and properties becomes particularly challenging. The use of a design of mixtures (DoM) approach facilitated the generation of polynomial equations indicating the influence of mixture components on various responses, enabling the prediction and optimization of glass properties over broad compositional ranges despite being within the anomalous region. This methodical approach not only advances our understanding of borate glass systems but also underscores the importance of predictive modelling in the accelerated design and development of novel glass materials for diverse applications.

Objective: This study's main goal is to investigate how deep learning approaches may be used to analyze stock market performance. The complex patterns and nonlinear interactions present in stock market data may be difficult to completely capture using traditional approaches, which are mostly based on statistical models. Methodology: Our work uses a large dataset of historical stock prices, macroeconomic indices, and other crucial financial factors to address this. Simple Moving Averages (SMA) are one of the feature engineering approaches that are used to combine fundamental and technical indicators. To capture the temporal dynamics of the stock market, the study goes further into a variety of deep learning architectures, including as long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Findings: The results show that thorough feature engineering combined with deep learning approaches may effectively capture the complexity of the stock market and provide forecasts that are more accurate. Implications: This highlights how deep learning may revolutionize financial market research and points to a paradigm change toward more trustworthy instruments for investors and decision-makers.