Enhanced Decision Support Research Articles

P-1869. Utilizing Large Language Models for Enhanced Decision Support in Travel Medicine Clinic: our experience at Mayo Clinic

Abstract Background The integration of Generative AI (GAI) into healthcare systems is increasingly recognized for its potential to transform patient management. The primary aim of this research was to evaluate and quantify the performance of large language models (LLMs) in generating actionable travel medicine advice. Architectural design of the Travel Clinic LLM project. Four phases of Discovery, Design, Evaluation, and Implementation/Deployment. Methods This study utilized two iterative phases of evaluation. In the initial phase, LLMs were prompted with detailed clinical scenarios including demographic data, medical and immunization histories, and specific travel plans. These prompts were designed to mimic typical inquiries encountered in travel consultations. The LLMs' initial responses were generated using the CDC’s Yellow Book as a foundational knowledge base. In the subsequent phase, the prompts were refined for greater specificity and clarity, and the knowledge base was enhanced by transitioning to Travax’s Travelers’ Health database. Additional structured data inputs included an exhaustive list of vaccines from our pharmacy formulary and a detailed table of vaccine contraindications. The responses were evaluated and scored by ID clinicians from the Mayo Clinic. Results Initial findings after first iteration revealed limited efficacy with recall at 23.9%, an F1 score of 38.6%, accuracy also at 23.9%, and precision maintained at 100%, utilizing the CDC's Yellow Book. With the implementation of Travax and refined prompting techniques, preliminary results suggest a notable improvement in the quality of responses, though detailed scoring is presently underway. Improvements in the LLM’s performance can be attributed to several key adjustments: the adoption of a more comprehensive knowledge base, refined prompt engineering, and the incorporation of structured data to support more accurate and detailed recommendations. The collaborative engagement of Mayo Clinic with Google and Travax facilitated a synergistic approach to optimizing the AI model's utility and integration. Future plans include embedding the LLM into our EMR system. Conclusion The findings from this study highlight the significance of strategic collaborations between large healthcare centers, IT industry, and specialized knowledge database firms in effectively harnessing GAI for clinical use. Disclosures All Authors: No reported disclosures

Optimization of Inbound and Outbound Vessel Scheduling in One-Way Channel Based on Reinforcement Learning

As the size and number of ships continue to grow, effective management of vessel scheduling has become more and more important for the efficient one-way channel port operation, whose characteristics significantly affect the safety and efficiency of ports. This paper presents a reinforcement-learning-based approach to optimize the scheduling of vessels in a one-way channel, aiming to quickly identify a scheduling solution that enhances port operational efficiency. This method models the vessel scheduling problem in a one-way channel by incorporating navigational constraints, safety requirements, and vessel-specific characteristics. Using the Q-learning algorithm to minimize vessel wait times, it identifies an optimal scheduling solution. Experiments were conducted using real data from the Dayao Bay Pier of Dalian Port to validate the rationality and effectiveness of the proposed model and algorithm. The results show that the reinforcement learning approach achieved approximately a 16% improvement in solution quality compared to the genetic algorithm (GA) while requiring only half the computation time. Additionally, it reduced delay times by over 40% relative to the traditional FCFS strategy, indicating superior overall performance. This research presents an efficient, intelligent approach to vessel scheduling, providing a theoretical foundation for further advancements in this field and enhancing decision support for vessel scheduling in one-way channels with practical implications.

LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems

This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses.

Can ChatGPT 4.0 Diagnose Epilepsy? A Study on Artificial Intelligence's Diagnostic Capabilities.

Objectives: This study investigates the potential of artificial intelligence (AI), specifically large language models (LLMs) like ChatGPT, to enhance decision support in diagnosing epilepsy. AI tools can improve diagnostic accuracy, efficiency, and decision-making speed. The aim of this study was to compare the level of agreement in epilepsy diagnosis between human experts (epileptologists) and AI (ChatGPT), using the 2014 International League Against Epilepsy (ILAE) criteria, and to identify potential predictors of diagnostic errors made by ChatGPT. Methods: A retrospective analysis was conducted on data from 597 patients who visited the emergency department for either a first epileptic seizure or a recurrence. Diagnoses made by experienced epileptologists were compared with those made by ChatGPT 4.0, which was trained on the 2014 ILAE epilepsy definition. The agreement between human and AI diagnoses was assessed using Cohen's kappa statistic. Sensitivity and specificity were compared using 2 × 2 contingency tables, and multivariate analyses were performed to identify variables associated with diagnostic errors. Results: Neurologists diagnosed epilepsy in 216 patients (36.2%), while ChatGPT diagnosed it in 109 patients (18.2%). The agreement between neurologists and ChatGPT was very low, with a Cohen's kappa value of -0.01 (95% confidence intervals, CI: -0.08 to 0.06). ChatGPT's sensitivity was 17.6% (95% CI: 14.5-20.6), specificity was 81.4% (95% CI: 78.2-84.5), positive predictive value was 34.8% (95% CI: 31.0-38.6), and negative predictive value was 63.5% (95% CI: 59.6-67.4). ChatGPT made diagnostic errors in 41.7% of the cases, with errors more frequent in older patients and those with specific medical conditions. The correct classification was associated with acute symptomatic seizures of unknown etiology. Conclusions: ChatGPT 4.0 does not reach human clinicians' performance in diagnosing epilepsy, showing poor performance in identifying epilepsy but better at recognizing non-epileptic cases. The overall concordance between human clinicians and AI is extremely low. Further research is needed to improve the diagnostic accuracy of ChatGPT and other LLMs.

The role of artificial intelligence in preoperative planning for Total Hip Arthroplasty: a systematic review

BackgroundTotal Hip Arthroplasty (THA) is a transformative surgical intervention for hip joint disorders, necessitating meticulous preoperative planning for optimal outcomes. With the emergence of Artificial Intelligence (AI), preoperative planning paradigms have evolved, leveraging AI algorithms for enhanced decision support and imaging analysis. This systematic review aims to comprehensively evaluate the role of AI in THA preoperative planning, synthesizing evidence from studies exploring various AI techniques and their applications.MethodsA systematic search of PubMed, Scopus, and Web of Science databases was conducted to identify relevant articles. Inclusion criteria encompassed studies focusing on AI in THA preoperative planning, including randomized controlled trials (RCTs), observational studies, and comparative studies.ResultsSix studies from China met the inclusion criteria, collectively analyzing 831 patients. AI-assisted planning demonstrated superior accuracy in estimating prosthesis size and positioning compared to traditional methods. However, limitations such as geographic bias and language constraints were noted.ConclusionAI-assisted preoperative planning significantly enhances femoral positioning accuracy, providing superior outcomes compared to traditional methods. This improvement in precision, particularly in the placement of femoral and acetabular components, has been consistently observed across studies, making AI an indispensable tool in improving the overall success of Total Hip Arthroplasty. Despite promising findings, further research is warranted to address limitations and optimize the integration of AI technologies into routine clinical practice.

Enhanced Decision Support for Multi-Objective Factory Layout Optimization: Integrating Human Well-Being and System Performance Analysis

This paper presents a decision support approach to enable decision-makers to identify no-preference solutions in multi-objective optimization for factory layout planning. Using a set of trade-off solutions for a battery production assembly station, a decision support method is introduced to select three solutions that balance all conflicting objectives, namely, the solution closest to the ideal point, the solution furthest from the nadir point, and the one that is best performing along the ideal nadir vector. To further support decision-making, additional analyses of system performance and worker well-being metrics are integrated. This approach emphasizes balancing operational efficiency with human-centric design, aligning with human factors and ergonomics (HFE) principles and Industry 4.0–5.0. The findings demonstrate that objective decision support based on Pareto front analysis can effectively guide stakeholders in selecting optimal solutions that enhance both system performance and worker well-being. Future work could explore applying this framework with alternative multi-objective optimization algorithms.

Advances in viticulture via smart phenotyping: current progress and future directions in tackling soil copper accumulation.

Modern viticulture faces significant challenges including climate change and increasing crop diseases, necessitating sustainable solutions to reduce fungicide use and mitigate soil health risks, particularly from copper accumulation. Advances in plant phenomics are essential for evaluating and tracking phenotypic traits under environmental stress, aiding in selecting resilient vine varieties. However, current methods are limited, hindering effective integration with genomic data for breeding purposes. Remote sensing technologies provide efficient, non-destructive methods for measuring biophysical and biochemical traits of plants, offering detailed insights into their physiological and nutritional state, surpassing traditional methods. Smart phenotyping is essential for selecting crop varieties with desired traits, such as pathogen-resilient vine varieties, tolerant to altered soil fertility including copper toxicity. Identifying plants with typical copper toxicity symptoms under high soil copper levels is straightforward, but it becomes complex with supra-optimal, already toxic, copper levels common in vineyard soils. This can induce multiple stress responses and interferes with nutrient acquisition, leading to ambiguous visual symptoms. Characterizing resilience to copper toxicity in vine plants via smart phenotyping is feasible by relating smart data with physiological assessments, supported by trained professionals who can identify primary stressors. However, complexities increase with more data sources and uncertainties in symptom interpretations. This suggests that artificial intelligence could be valuable in enhancing decision support in viticulture. While smart technologies, powered by artificial intelligence, provide significant benefits in evaluating traits and response times, the uncertainties in interpreting complex symptoms (e.g., copper toxicity) still highlight the need for human oversight in making final decisions.

Pilot deployment of a machine-learning enhanced prediction of need for hemorrhage resuscitation after trauma – the ShockMatrix pilot study

ImportanceDecision-making in trauma patients remains challenging and often results in deviation from guidelines. Machine-Learning (ML) enhanced decision-support could improve hemorrhage resuscitation.AimTo develop a ML enhanced decision support tool to predict Need for Hemorrhage Resuscitation (NHR) (part I) and test the collection of the predictor variables in real time in a smartphone app (part II).Design, setting, and participantsDevelopment of a ML model from a registry to predict NHR relying exclusively on prehospital predictors. Several models and imputation techniques were tested. Assess the feasibility to collect the predictors of the model in a customized smartphone app during prealert and generate a prediction in four level-1 trauma centers to compare the predictions to the gestalt of the trauma leader.Main outcomes and measuresPart 1: Model output was NHR defined by 1) at least one RBC transfusion in resuscitation, 2) transfusion ≥ 4 RBC within 6 h, 3) any hemorrhage control procedure within 6 h or 4) death from hemorrhage within 24 h. The performance metric was the F4-score and compared to reference scores (RED FLAG, ABC). In part 2, the model and clinician prediction were compared with Likelihood Ratios (LR).ResultsFrom 36,325 eligible patients in the registry (Nov 2010—May 2022), 28,614 were included in the model development (Part 1). Median age was 36 [25–52], median ISS 13 [5–22], 3249/28614 (11%) corresponded to the definition of NHR. A XGBoost model with nine prehospital variables generated the best predictive performance for NHR according to the F4-score with a score of 0.76 [0.73–0.78]. Over a 3-month period (Aug—Oct 2022), 139 of 391 eligible patients were included in part II (38.5%), 22/139 with NHR. Clinician satisfaction was high, no workflow disruption observed and LRs comparable between the model and the clinicians.Conclusions and relevanceThe ShockMatrix pilot study developed a simple ML-enhanced NHR prediction tool demonstrating a comparable performance to clinical reference scores and clinicians. Collecting the predictor variables in real-time on prealert was feasible and caused no workflow disruption.

Impact of Management Information Systems on Enterprise Decision Support

This thesis examines the impact of management information systems (MIS) on decision support in business, focusing on the role of MIS in improving data availability, enhancing data analysis capabilities, improving decision-making efficiency and supporting collaborative decision-making. Through real-world case studies of large manufacturing companies and retail businesses, the paper demonstrates how MIS implementation can significantly improve productivity, inventory management, and customer satisfaction. The manufacturing company in Case 1 improved production line efficiency and inventory management, reduced inventory costs, and enhanced decision support by integrating an MIS platform. The retail enterprise in Case 2 achieved sales growth, inventory optimisation and customer satisfaction through real-time data analytics and CRM systems. The paper concludes that the application of MIS in modern enterprises can effectively improve the quality of decision-making and operational efficiency, and provide important support for enterprises in complex market environments. Through an in-depth analysis of MIS functions and their impact, the paper provides useful references and lessons for enterprises in the implementation and optimisation of decision support systems.

Enhancing decision support system for finished vehicle logistics service provider selection through a single-valued neutrosophic Dombi Bonferroni-based model

Finished vehicle logistics (FVL) activities are executed through agreements with service provider firms. Vehicle manufacturers make efforts to select the most successful FVL service provider. The motivation for this research lies in developing and demonstrating the application of a decision support system for FVL service provider selection. Within this scope, it is necessary to develop a decision model that includes both quantitative and qualitative criteria. Considering the decision process as a group decision-making process allows for simultaneous consideration of different perspectives on the decision. Single-valued neutrosophic (SVN) sets are used for expert evaluations. The SVN weighted Dombi Bonferroni mean aggregation operator is employed for criteria weighting. The SVN-alternative ranking using two-step logarithmic normalization (ARLON) method is developed for ranking FVL service providers. The feasibility of the SVN-Dombi Bonferroni-ARLON hybrid algorithm is supported by the case study application. It is concluded that supply chain and logistics managers hold greater importance in FVL service provider selection, with the number of ships being the most critical selection criterion. The robustness of the proposed hybrid method for FVL service provider selection is supported by sensitivity analyses, while its consistency is supported by comparative analyses.

Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis

Background: Nonsurgical treatment of uncomplicated appendicitis is a reasonable option in many cases despite the sparsity of robust, easy access, externally validated, and multimodally informed clinical decision support systems (CDSSs). Developed by OpenAI, the Generative Pre-trained Transformer 3.5 model (GPT-3) may provide enhanced decision support for surgeons in less certain appendicitis cases or those posing a higher risk for (relative) operative contra-indications. Our objective was to determine whether GPT-3.5, when provided high-throughput clinical, laboratory, and radiological text-based information, will come to clinical decisions similar to those of a machine learning model and a board-certified surgeon (reference standard) in decision-making for appendectomy versus conservative treatment. Methods: In this cohort study, we randomly collected patients presenting at the emergency department (ED) of two German hospitals (GFO, Troisdorf, and University Hospital Cologne) with right abdominal pain between October 2022 and October 2023. Statistical analysis was performed using R, version 3.6.2, on RStudio, version 2023.03.0 + 386. Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. Statistical significance was defined as p < 0.05. Results: There was agreement between the surgeon’s decision and GPT-3.5 in 102 of 113 cases, and all cases where the surgeon decided upon conservative treatment were correctly classified by GPT-3.5. The estimated model training accuracy was 83.3% (95% CI: 74.0, 90.4), while the validation accuracy for the model was 87.0% (95% CI: 66.4, 97.2). This is in comparison to the GPT-3.5 accuracy of 90.3% (95% CI: 83.2, 95.0), which did not perform significantly better in comparison to the machine learning model (p = 0.21). Conclusions: This study, the first study of the “intended use” of GPT-3.5 for surgical treatment to our knowledge, comparing surgical decision-making versus an algorithm found a high degree of agreement between board-certified surgeons and GPT-3.5 for surgical decision-making in patients presenting to the emergency department with lower abdominal pain.

Measuring exploration: evaluation of modelling to generate alternatives methods in capacity expansion models

Abstract As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.

A-165 Value Of Monocyte Distribution Width In Bacteremia Assessment In Emergency Department Patients

Abstract Background Monocyte distribution width (MDW) is an immune response biomarker and part of a CBC differential. MDW is derived from the distribution of monocyte volumes and elevated MDW values correlate with severe infections and sepsis in Emergency Department (ED) adult patients. Neutrophil to lymphocyte ratio (NLR) is predictive of infections and inflammatory stress. Evaluating these biomarkers could be valuable in bacteremia risk assessment. Bacteremia is the presence of viable bacteria in the bloodstream and is present in up to 20% of septic patients. Blood cultures establish pathogen identity but require culture time, potentially delaying appropriate treatment. Our goal was to characterize MDW and NLR clinical performance in ED patients with suspected bacteremia. Methods This was an observational cohort study of ED patients 18 years and older with CBC differential and blood cultures obtained. MDW values ≥20 is the published cut-off for increased infection risk. Clinical literature suggests that NLR ≥3 is abnormal. We assessed the diagnostic performance of MDW and NLR for positive blood cultures using SPSS Version 25. Results From 07/2021-09/2023, 9,400 blood cultures were ordered and 5174 patients had differential CBCs. The overall blood culture positivity rate was 14.4%. MDW and NLR performance for positive blood cultures is shown in Table 1. Conclusions Our analysis provides evidence that MDW and NLR may be useful for early recognition of bacteremia in ED patients. CBC differential results are reported sooner than culture results. MDW is directly reported, NLR can be readily calculated from the differential. Therefore both could contribute to earlier risk stratification and our findings suggest potentially distinct information from MDW and NLR. Combining MDW with other CBC parameters may provide additional diagnostic advantage. Further analysis will incorporate added clinical data for enhanced decision support in interpreting MDW values in the context of pending blood culture results.

Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing

ABSTRACT Machine learning models for water quality prediction often face challenges due to insufficient data and uneven spatial-temporal distributions. To address these issues, we introduce a framework combining machine learning, numerical modeling, and remote sensing imagery to predict coastal water turbidity, a key water quality proxy. This approach was tested in the Great Lakes region, specifically Cleveland Harbor, Lake Erie. We trained models using observed data and synthetic data from 3D numerical models and tested them against in situ and remote sensing data from PlanetLabs' Dove satellites. High-resolution (HR) data improved prediction accuracy, with RMSE values of 0.154 and 0.146 log10(FNU) and R2 values of 0.92 and 0.93 for validation and test datasets, respectively. Our study highlights the importance of unified turbidity measures for data comparability. The machine learning model demonstrated skill in predicting turbidity through transfer learning, indicating applicability in diverse, data-scarce regions. This approach can enhance decision support systems for coastal environments by providing accurate, timely predictions of water quality variables. Our methodology offers robust strategies for turbidity and water quality monitoring and has potential for improving input data quality for numerical models and developing predictive models from remote sensing data.

Comparative analysis of machine learning models for rainfall prediction

Predicting rainfall is essential for many applications, including agriculture, hydrology, and disaster management. In this work, we undertake a comparison examination of various machine learning models to forecast rainfall based on meteorological data. The target variable in this study is rainfall, and the dataset used includes characteristics like temperature, relative humidity, wind speed, and wind direction. The following seven machine learning models were assessed: Support Vector Regression (SVR), Multivariate adaptive regression splines (MARS), Random Forest Regression, and Deep Neural Network with Historical Data (DWFH), Haar Wavelet Function, Decision Tree and Discrete wavelet Transform (DWT). Data preprocessing, which includes standardisation and lagging to capture temporal dependencies, comes first in the analysis phase. A wavelet transformation is also used to capture complex patterns in the data. Each model is tested on a different test set after being trained on a subset of the dataset. The results are assessed using the Root Mean Squared Error (RMSE) and Mean Squared Error (MSE), focusing on the RMSE and MSE values for better comparison across models. Our findings reveal that the DWFH model achieved an RMSE of 0.0138807 mm and MSE of 0.000193 mm2, demonstrating their effectiveness in predicting rainfall. The Random Forest and SVR models also provided competitive results. This study highlights the importance of selecting an appropriate machine learning model for rainfall prediction and the significance of preprocessing techniques in improving model performance. These insights can aid decision-makers in choosing the most suitable model for their specific application, contributing to more accurate rainfall predictions and enhanced decision support systems.

Real-time data warehousing in the big data environment: A comprehensive review of implementation in the internet industry

A real-time data warehouse is a crucial tool for information management and analysis, enabling the capture, processing, and analysis of vast amounts of data from diverse sources in real-time. It offers enterprises enhanced decision support through its efficient processing capabilities and timely data feedback. This paper reviews the technical characteristics and application scenarios of real-time data warehouses, with a particular focus on the Internet sector. It explores the evolution from traditional data warehouses to modern data lake and lakehouse architectures, emphasizing the advancements in data processing capabilities, including the separation of storage and compute functions. Real-time data warehouses, which enable immediate data processing and feedback, are essential for enterprises requiring up-to-the-minute insights. The study compares the Lambda and Kappa architectures, detailing their strengths and weaknesses in terms of data throughput, latency, and scalability. Innovations such as Apache Hudi and lakehouse architectures offer new opportunities for performance optimization and functional expansion. The emergence of hybrid architectures like HTAP (Hybrid Transactional/Analytical Processing) and HSAP (Hybrid Serving/Analytical Processing) represents a significant advancement in integrating transactional and analytical processing. Future research should focus on the impact of artificial intelligence and machine learning on real-time data warehouses to enhance their analytical and predictive capabilities, reduce complexity, and lower operational costs.

Retraction Note: Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Retraction Note: Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

An integrated MCDM model with enhanced decision support in transport safety using machine learning optimization

Having the capacity to effectively manage multi-criteria decision-making (MCDM) issues with considerable reliability is a crucial prerequisite for any MCDM model. This study introduces an advanced MCDM model that integrates logarithmic percentage change-driven objective weighting (LOPCOW), multi-objective optimization on the basis of a ratio analysis plus the full multiplicative form (MULTIMOORA), and density-based spatial clustering of applications with noise (DBSCAN). The goal is to provide robust decision support. Specifically, the model incorporates a machine learning tool, namely, grid search, to optimize the parameters of neighborhood radius (ε) and the minimum number of points, which significantly influence the clustering quality of DBSCAN. Subsequently, the cluster centers determined by DBSCAN serve as initial points for k-means clustering to negate the impact of outliers. This approach addresses the failures of DBSCAN in nearest neighbor search and parameter selection, and it also resolves the limitations of k-means related to the uncertainty in cluster outcomes due to varying initial centroid selections. Utilizing a real case study from the European Union, comprehensive comparisons of the model results demonstrate the adaptability, reliability, and stability of the proposed model, affirming its effectiveness in providing credible policy recommendations and practical decisions. Overall, this study presents policymakers, professionals, and administrators with a reliable decision-making system for conducting real-world MCDM tasks with enhanced confidence and robustness.

Causal Machine Learning in Marketing

This study reviews three primary purposes of causal machine learning (CML) in marketing, merging impact evaluation of marketing interventions with machine learning algorithms for learning statistical patterns from data. Firstly, CML enables more credible impact evaluation by considering important control variables that simultaneously influence the intervention and business outcomes (such as sales) in a data-driven manner. Secondly, it facilitates the data-driven detection of customer segments for which a marketing intervention is particularly effective or ineffective, a process known as effect moderation or heterogeneity analysis. Thirdly, closely related to the second point, it allows for optimal customer segmentation into groups that should and should not be targeted by the intervention to maximize overall effectiveness. The discussion is grounded in recent empirical applications, all of which aim to enhance decision support in marketing by leveraging data-driven evaluation and optimization of interventions across different customer groups.

LiDAR and maps blend for rural decision support

AbstractThis study integrates aerial LiDAR data and 2D cartographic information to rapidly develop an advanced non‐photorealistic rendering (NPR) model for rural environment analysis. The focus is enhancing decision support in crises and assessing potential hazards in these territories. The methodology involves capturing LiDAR data from high altitudes and classifying it as Ground, Vegetation, and Buildings. The integration of this data with 2D cartographic information, augmented with attribute data from a GIS database, is achieved through a semi‐automatic process. This process facilitates the creation of detailed 3D models, providing a more nuanced, visually and semantically rich representation of the rural landscape. The study underscores the benefits of combining LiDAR, photogrammetric, and cartographic data for creating accurate and detailed models of the rural environment, which are crucial for effective decision‐making and threat assessment.