Development Of Decision Support System Research Articles

Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-GIS DSS (Web-Geographic Information System Decision Support System) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and OECD (Organization for Economic Corporation and Development) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, IranThe developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

Development of Promotional Criteria Decision Support System to Increase Sales of Scarlett Whitening Products through Digital Marketing Strategy

This research delves into the multifaceted landscape of promoting skin whitening products in Indonesia, focusing on consumer behavior, digital marketing strategies, and the development of a Promotion Criteria Decision Support System (PCDSS). Through qualitative analysis of participant interviews and simulated interviews, key insights were gleaned. Consumer preferences, heavily influenced by cultural ideals, prioritize fair skin and product safety and efficacy. Digital marketing emerged as a potent tool, with social media platforms and influencer partnerships driving engagement. The necessity of a tailored PCDSS integrating cultural nuances, regulatory compliance, and consumer insights was underscored. This study provides actionable insights for skincare brands aiming to navigate the Indonesian market effectively.

Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India

Abstract. This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–2022 shows high accuracy (up to 80 %) and a very low false alarm ratio (∼ 20 %) from day 1 to day 5 of the forecasts, especially when the ambient air quality index (AQI) is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the National Capital Region (NCR)'s districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to the PM2.5 load in Delhi. During peak pollution events (stubble-burning periods or wintertime), however, the contribution from the main sources (farm fires in Punjab–Haryana or local sources within Delhi) could reach 65 %–69 %. According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios.

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

This paper covers the use and influence of data-driven decision support systems (DSS) on drug management, particularly in the areas of drug exploration and production. The study, which employed a mixed-methods approach, involving literature review, qualitative interviews, quantitative assessments, and case study analysis, reveals the AI, machine learning and big data analytics ability to drive drug discovery and development processes revolution in the pharmaceutical industry. The study shows that handling of DSS allows sound decision making, consequently resulting in improved efficiency, decreased expenses and better innovation throughout the drug development process. Areas like data integration, algorithm robustness, and regulation are identified as the major issues, which offers an insight into the importance of these areas of concern in the effective application of a data-driven approach in pharmaceutical management. Such contrast with the like works underlines the role of our findings as an essential link in the AI-driven healthcare innovations chain. The research provides a foundation for further improving the state of knowledge and understanding of DSS in drug management, guiding future research aiming at expanding conceptual frameworks and designing practical implementations.

To NIRS or not: understanding clinical decision-making of respiratory support management related to acute respiratory failure using Critical Decision Method

Clinical decisions related to noninvasive respiratory support (NIRS) for acute respiratory failure patients are complex. NIRS strategies have favorable benefits for patients when successful but NIRS failure comes with longer ICU stays, increased mortality, and more intubation-related and mechanical ventilator-associated complications. Our goal is to understand how physicians make decisions regarding NIRS and explore the differences between emergency physicians and critical care physicians—who commonly make these decisions. We used the critical decision method to conduct ten semi-structured interviews with five emergency physicians and five critical care physicians from ten different healthcare systems across the United States. We found that physicians similarly use objective and subjective clinical cues but differentially use temporal and contextual cues in their decision-making. Several insights identified in our study can be helpful in the design and development of clinical decision support systems for respiratory support decision-making. Specifically, we recommend two customized distinct clinical decision support systems for respiratory support decision-making, one for each designated clinical setting (i.e., ED and ICU) with tailored designs that support the different decision-making processes.

Development and validation of a clinical decision support system to prevent anticoagulant duplications

Background and ObjectiveUnintended duplicate prescriptions of anticoagulants increase the risk of serious adverse events. Clinical Decision Support Systems (CDSSs) can help prevent such medication errors; however, sophisticated algorithms are needed to avoid alert fatigue. This article describes the steps taken in our hospital to develop a CDSS to prevent anticoagulant duplication (AD). MethodsThe project was composed of three phases. In phase I, the status quo was established. In phase II, a clinical pharmacist developed an algorithm to detect ADs using daily data exports. In phase III, the algorithm was integrated into the hospital's electronic health record system. Alerts were reviewed by clinical pharmacists before being sent to the prescribing physician. We conducted a retrospective analysis of all three phases to assess the impact of the interventions on the occurrence and duration of ADs. Phase III was analyzed in more detail regarding the acceptance rate, sensitivity, and specificity of the alerts. ResultsWe identified 91 ADs in 1641 patients receiving two or more anticoagulants during phase I, 70 ADs in 1696 patients in phase II, and 57 ADs in 1576 patients in phase III. Mean durations of ADs were 1.8, 1.4, and 1.1 calendar days during phases I, II, and III, respectively. In comparison to the baseline in phase I, the relative risk reduction of AD in patients treated with at least two different anticoagulants during phase III was 39% (RR: 0.61, CI: 0.44–0.85). A total of 429 alerts were generated during phase III, many of which were self-limiting, and 186 alerts were sent to the respective prescribing physician. The acceptance rate was high at 97%. We calculated a sensitivity of 87.4% and a specificity of 87.9%. ConclusionThe stepwise development of a CDSS for the detection of AD markedly reduced the frequency and duration of medication errors in our hospital, thereby improving patient safety.

[Artificial intelligence in intensive care medicine].

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

Utilizing Fundamental Analysis to Predict Stock Prices

Portfolio management involves the critical task of determining the optimal times to enter or exit a stock in order to maximize profits in the stock market. Unfortunately, many retail investors struggle with this task due to unclear investment objectives and a lack of a structured decision-making process. With the vast number of stocks available in the market, it can be difficult for investors to determine which stocks to invest in. As a result, there is a growing need for the development of effective investment decision support systems to assist investors in making informed decisions. Researchers have explored various approaches to building such systems, including predicting stock prices using sentiment analysis of news, articles, and social media, as well as historical trends and patterns. However, the impact of financial reports filed by companies on stock prices has not been extensively studied. This paper aims to address this gap by using machine learning techniques to develop a more accurate stock prediction model based on financial reports from companies in the Nifty 50. The financial reports considered include quarterly reports, annual reports, cash flow statements, and ratios.

Integrated design of a sustainable waste management system with co-modal transportation network: A robust bi-level decision support system

Efficient waste management practices play a critical role in addressing the acute challenges of environmental protection, public health and resource conservation. A well-designed system guarantees that waste is efficiently collected, treated and disposed while minimizing negative impacts on ecosystems and human well-being. This work presents a robust bi-level decision support system to establish a sustainable waste management system using a co-modal transportation network to treat municipal solid waste timely and efficiently. Consequently, two integrated multi-objective mathematical models are developed to formulate the problem. Configuring the municipal solid waste network in the first level of the suggested decision support system, the transportation network is designed in the second level taking into account non-identical modes. The objectives are to minimize total cost and total emission in both levels, while maximization of total job creation is also addressed in the first level. Robust optimization method and weighted goal programming method are then utilized to accommodate the developed decision support system against uncertainty and multi-objectiveness, respectively. To validate the efficiency of these methods, they are assessed against possibilistic linear programming technique and Lp-metric approach with the help of simple additive weighting (SAW) method, respectively. Eventually, several numerical examples are generated based on the benchmarks given in the literature, which are then tackled using CPLEX solve to appraise the applicability and complexity of the developed methodology. The findings reveal the efficacy of the decision support system in terms of finding solutions in less than 448 s on average. Finally, sensitivity analyses are performed to draw out useful practical implications and decision aids.

МЕТОДОЛОГИЯ СОЗДАНИЯ ИНДИВИДУАЛЬНОЙ МОДЕЛИ ЗДОРОВЬЯ ЧЕЛОВЕКА С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Background. In the modern world, the role of artificial intelligence in healthcare is becoming increasingly significant, providing new opportunities to transform traditional methods of diagnosis, treatment and medical data management. This technological breakthrough not only improves the efficiency of medical procedures, but also opens up new per-spectives in the prevention and treatment of diseases. The aim. To emphasize the need to create a personalized health model using artificial intelligence to help an individual achieve and maintain optimal health and well-being. In the field of personalized treatment, artificial intelligence plays an important role, taking into account the unique characteristics of each patient. Algorithms analyze genetic information, medical history and responses to previous therapies to develop optimal treatment plans. This opens the way to individualized medicine, where the approach to each patient is based on his or her unique characteristics. Despite all the positive aspects, the introduction of artificial intelligence in healthcare also raises questions of data privacy, ethical issues and technology security. However, if these issues are resolved, artificial intelligence promises to significantly improve the quality and accessibility of medical treatment, opening new horizons in healthcare. Results. In this article, we describe breakthroughs in artificial intelligence technologies and biomedical applications, identify problems of using and further development in medical artificial intelligence systems and summarize the economic, legal and social consequences of using artificial intelligence in healthcare, and propose a scheme for constructing a model of individual human health using artificial intelligence. Conclusion. The results of the analysis of modern scientific literature allow us to draw a conclusion about the potential for creating more effective and personalized approaches to the problem of individual health using integrated artificial intelligence technologies. The proposed methodology can serve as the basis for the development of innovative deci-sion support systems in medicine and improving the quality of medical care.

Development of Decision Support System (DSS) for surface runoff and erosion control structures planning: a case study in the Upper Citarum Watershed & towards sustainability inland waters ecosystem

The watershed ecosystem is a complex system in which there are numerous transportation and transfer mechanisms of mass and energy. To make management more effective in all national critical watersheds, there needs to be more scientific, evidence-based policymaking that is based on an understanding of the system and mechanisms of the socio-hydrological processes of the watershed. The availability of Decision Support System (DSS) technology can be the appropriate approach to this need because DSS can be an interface between scientific and practical needs (easy-to-use, easy-to-access, user-friendly). However, the availability and implementation of DSS as an important tool in the optimal design of sustainable watershed management in Indonesia are still very limited. This study aims to create a prototype DSS that practitioners and policymakers can use to identify priority areas and optimize technical solutions for controlling surface runoff and soil erosion at various scales in the internal watershed. Herein, the spatial-based numerical modeling system and process mechanism; the database and knowledge; and the Graphical User Interface (GUI) are the three main components that have been used as a framework for model-based DSS development. A distributed rainfall-runoff-erosion model (EcoHydro) is the main engine of DSS for spatially quantifying dimensions of hydrological responses, erosion rate, and sediment production according to the user’s specifications and providing design options for control measures of them. The upper Citarum river basin in West Java, which is a critical and first national priority watershed, has been selected as the case study for DSS development and to demonstrate its application. Furthermore, the resulting DSS can later be developed for use in watersheds and other nationally critical lakes.

The process of clinical decision-making in chronic wound care: A scenario-based think-aloud study

AimsTo undertake a comprehensive investigation into both the process of information acquisition and the clinical decision-making process utilized by primary care nurses in the course of treating chronic wounds. DesignScenario-based think-aloud method, enriched by the integration of information processing theory. The study was conducted within the framework of home care nursing organizations situated in [placeholder]. A cohort of primary care nurses (n = 10), each possessing a minimum of one year of nursing experience, was recruited through the collaboration of three home care nursing organizations. MethodsTwo real-life clinical practice scenarios were employed for the interviews, with the researcher adopting the roles of either the patient or another clinician to enhance the realism of the think-aloud process. Each think-aloud session was promptly succeeded by a subsequent follow-up interview. The Consolidated criteria for Reporting Qualitative research checklist was followed to guarantee a consistent and complete report of the study. ResultsAmidst noticeable variations, a discernible pattern surfaced, delineating three sequential concepts: 1. gathering overarching information, 2. collecting and documenting wound-specific data, and 3. interpreting information to formulate wound treatment strategies. These concepts encompassed collaborative discussions with stakeholders, while the refinement of wound treatment strategies was interwoven within both concepts 2 and 3. ConclusionsEvident variations were identified in chronic wound care clinical decision-making, regardless of educational background or experience. These insights hold the potential to inform the development of clinical decision support systems for chronic wound management and provide guidance to clinicians in their decision-making endeavours.

The Development and Usability Assessment of an Augmented Reality Decision Support System to Address Burn Patient Management

Critical care injuries, such as burn trauma, require specialized skillsets and knowledge. A clinical decision support system to aid clinicians in providing burn patient management can increase proficiency and provide knowledge content for specific interventions. In austere environments, decision support tools can be used to aid in decision making and task guidance when skilled personnel or resources are limited. Therefore, we developed a novel software system that utilizes augmented reality (AR) capabilities to provide enhanced step-by-step instructions based on best practices for managing burn patients. To better understand how new technologies, such as AR, can be used for burn care management, we developed a burn care application for use on a heads-up display. We developed four sub-set applications for documenting and conducting burn wound mapping, fluid resuscitation, medication calculations, and an escharotomy. After development, we conducted a usability study utilizing the System Usability Scale, pre- and post- simulation surveys, and after-action reviews to evaluate the AR-based software application in a simulation scenario. Results of the study indicate that the decision support tool has generalized usability and subjects were able to use the software as intended. Here we present the first use case of a comprehensive burn management system utilizing augmented reality capabilities to deliver care.

Размещение деталей-катодов при управлении гальванической ванной по векторному критерию

The problem of controlling a galvanic bath according to two criteria is considered: marriage in terms of un-evenness and microhardness. The controlling action is the placement of cathode parts of different shapes and sizes on the suspension device of the galvanic bath. A combined method is proposed, which consists in repeatedly solving the vector optimization problem with different order ratios and with different convolutions of criteria, as well as by analyzing hierarchies and constructing a Pareto domain. The developed decision support system provides the decision-maker with comprehensive information about the operating modes of the object and the values of criteria obtained at the same time, the values of mutual changes in criteria, etc., which makes it possible to find the best compromise solution to the vector optimization problem from the user's point of view.

The role and significance of operations research in management

The article explores the pivotal role of Operations Research (OR) in contemporary management, tracing its origins from World War II to its evolution into an indispensable tool for optimizing decision-making in various industries. OR, characterized by mathematical modelling, statistical analysis, and optimization techniques, aligns seamlessly with management objectives, enhancing efficiency, resource allocation, and strategic decision-making. The multifaceted contributions of OR are elucidated, encompassing its role in strategic decision-making, the development of Decision Support Systems, resource allocation, risk management, and interdisciplinary collaboration. Emphasizing the dynamic and multifaceted nature of OR, the article underscores its significance in shaping effective management strategies. The core tenet of optimization becomes a catalyst for efficiency gains and strategic resource utilization. As organizations grapple with the complexities of a rapidly evolving business landscape, the integration of OR principles is portrayed not merely as beneficial but imperative for sustained success. It emerges as a guiding philosophy, empowering organizations to navigate uncertainties, make informed decisions, and foster innovation and excellence. The synergy between Operations Research and management is positioned as a strategic imperative, steering organizations towards efficiency, innovation, and long-term prosperity in an era where data-driven decision-making is paramount.

The application of operation research in the design of on-site construction organization

This article explores the integration of Operations Research (OR) principles in the design and management of on-site construction organizations. The first section introduces Operations Research as a multidisciplinary field that employs mathematical modelling, statistical analysis, and optimization techniques to enhance decision-making processes across various sectors. Key aspects such as mathematical modelling, optimization, decision analysis, simulation, queueing theory, linear programming, and game theory are highlighted. The second section focuses on the design of on-site construction organizations, emphasizing the structure and organization of resources, personnel, and processes essential for efficient project execution. Various key elements, including project management, site personnel, project support teams, engineering and design teams, health and safety personnel, procurement and supply chain, communication and coordination, equipment and maintenance, and environmental compliance, are discussed. The third section delves into the application of Operations Research techniques in the design and management of on-site construction organizations. It outlines how OR principles can be employed for resource allocation and optimization, project scheduling and planning, inventory management, equipment utilization, project costing and budgeting, risk management, supply chain management, human resource management, facility layout and design, and the development of decision support systems. The conclusion highlights that the strategic application of Operations Research methodologies contributes to enhanced efficiency, optimized resources, and improved decision-making throughout the construction project lifecycle. The integration of OR principles not only enhance the overall effectiveness of on-site construction organizations but also leads to cost savings, timely project completion, and improved project outcomes. As construction projects continue to grow in scale and complexity, the systematic and analytical approach provided by Operations Research becomes increasingly valuable in addressing the intricate challenges faced by construction stakeholders.

Development of a clinical decision support system for CVD screening based on artificial intelligence using polysomnographic records

Development of a clinical decision support system for CVD screening based on artificial intelligence using polysomnographic records

Pattern Recognition and Anomaly Detection in fetal morphology using Deep Learning and Statistical learning (PARADISE): protocol for the development of an intelligent decision support system using fetal morphology ultrasound scan to detect fetal congenital anomaly detection

IntroductionCongenital anomalies are the most encountered cause of fetal death, infant mortality and morbidity. 7.9 million infants are born with congenital anomalies yearly. Early detection of congenital anomalies facilitates life-saving...

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care.

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.