Decision Support System Framework Research Articles

Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation.

Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques

Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the “Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms. Artificial Intelligence has been a boon for medicine and is regularly used to diagnose various health conditions and diseases. Hence, this study used demographic, emotional, and physical symptoms and multiple machine learning (ML) techniques to diagnose SAD. A thorough descriptive and statistical analysis has been conducted before using the classifiers. Among all the models, the AdaBoost and logistic regression obtained the highest accuracy of 88 % each. Four eXplainable artificial techniques (XAI) techniques are utilized to make the predictions interpretable, transparent and understandable. According to XAI, the “Liebowitz Social Anxiety Scale questionnaire” and “The fear of speaking in public” are the most critical attributes in the diagnosis of SAD. This clinical decision support system framework could be utilized in various suitable locations such as schools, hospitals and workplaces to identify SAD in people.

Enhancing Medical Decision Making: A Semantic Technology-Based Framework for Efficient Diagnosis Inference

In the dynamic landscape of healthcare, decision support systems (DSS) confront continuous challenges, especially in the era of big data. Background: This study extends a Q&A-based medical DSS framework that utilizes semantic technologies for disease inference based on a patient’s symptoms. The framework inputs “evidential symptoms” (symptoms experienced by the patient) and outputs a ranked list of hypotheses, comprising an ordered pair of a disease and a characteristic symptom. Our focus is on advancing the framework by introducing ontology integration to semantically enrich its knowledgebase and refine its outcomes, offering three key advantages: Propagation, Hierarchy, and Range Expansion of symptoms. Additionally, we assessed the performance of the fully implemented framework in Python. During the evaluation, we inspected the framework’s ability to infer the patient’s disease from a subset of reported symptoms and evaluated its effectiveness in ranking it prominently among hypothesized diseases. Methods: We conducted the expansion using dedicated algorithms. For the evaluation process, we defined various metrics and applied them across our knowledge base, encompassing 410 patient records and 41 different diseases. Results: We presented the outcomes of the expansion on a toy problem, highlighting the three expansion advantages. Furthermore, the evaluation process yielded promising results: With a third of patient symptoms as evidence, the framework successfully identified the disease in 94% of cases, achieving a top-ranking accuracy of 73%. Conclusions: These results underscore the robust capabilities of the framework, and the enrichment enhances the efficiency of medical experts, enabling them to provide more precise and informed diagnostics.

Construction and optimization of vending machine decision support system based on improved C4.5 decision tree

The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.

A synergistic study on farmers’ income enhancement and urban-rural financial development under the perspective of common wealth by an intelligent decision support system for land improvement

Abstract This paper proposes the general framework and technical process of the intelligent decision support system for land improvement based on a multi-objective artificial immunity algorithm. Based on the analysis of the current situation of land use structure and spatial layout in County S, three land resource quantity structure optimization schemes are selected, and the improvement of agricultural land use efficiency and agricultural output value in the three schemes are evaluated and analyzed. The mechanisms that affect income distribution in urban-rural financial development and land finance are summarized. The results show that after land improvement, the area of cultivated land and grassland in County S decreased by 1,444 ha, and the area of garden land and forest land increased to the greatest extent in the economically prioritized scenario. The size of towns increased to 541 ha, and the size of villages decreased to different degrees and all three land improvement programs had the combined effect of increasing the gross agricultural product and boosting farmers’ incomes. The development of urban and rural finance is aided by land improvement, but the degree of inequality in income distribution is widened.

From data to design: AI practice in design decisions

Abstract Facing the increasingly fierce market competition, enterprises need to make correct marketing decisions. Based on the decision support system, cloud server, and SOA framework, the article establishes an enterprise intelligent marketing decision-making system by combining the TCP/IP communication protocol and designing a data warehouse to store the marketing decision-making data. The Apriori algorithm in association rules is used to generate marketing rules to assist marketing decision-making, the marketing data is classified using the random forest algorithm, and the sparrow search algorithm optimizes the parameters of the random forest model. In practice, simulation experiments were designed to analyze the performance of the random forest model and the marketing decision-making system, and they were applied to the marketing decision-making example of the e-commerce platform. It was found that the AUC value obtained when the number of binary trees of the random forest model was 75 was 0.836, and the number of conflicts per second of the system was only 0.41 when the number of concurrent users was 5*104. The use of association rules resulted in eight marketing decisions, with the highest probability of user purchase prediction reaching 98.54%. Based on the intelligent marketing decision system, it can help decision-makers better analyze market changes and provide decision support for enterprises to develop scientific and reasonable marketing strategies.

Decision Support System for Strategic Planning in Educational Organization: A Survey

Strategic planning models and the provision of information for decision making in complex strategic circumstances are research topics of great interest. This research addresses the issue of supporting strategic planning decision-making in educational foundations by building a Decision Support System (DSS) to be used by decision-makers in carrying out their strategic planning process, the DSS is integrated in the entire organisation's information system database. This research provides an overview of DSS, the college planning process, the development of DSS through the use of artificial intelligence, and a framework for planning activities at different levels of the organisation to develop strategic plans. Based on the strategic planning process model, a DSS framework is proposed, along with decision support approaches for various DSS modules. DSS provides the best support (at individual, group, and organisational levels) for all stages of strategic planning decision-making. By applying DSS, it is possible to create more perfect circumstances to achieve sustainable future-orientated institutional goals.

Enhancing energy sustainability of building projects through nature-based solutions: A fuzzy-based decision support system

Statement of the Problem. Lack of an artificial intelligence (AI)-driven approach to optimize the application of nature-based solutions (NbS) for enhancing energy sustainability in building projects. This gap can result in suboptimal decision-making processes. Purpose. This study aims at introducing a decision support system (DSS) framework that fuses NbS with the power of fuzzy – based AI. The proposal seeks to support decision-makers and empower them with the capabilities of AI. Method. Through a systematic literature review, the aim was to first capture models’ input variables for the development of a DSS framework using fuzzy logic. Conclusions. Our findings underscore the significance of an integrated approach in energy-efficient building projects. The integration of NbS with fuzzy-based AI showcases substantial promise in augmenting decision-making processes, promoting optimized designs that align with both environmental and technological objectives. Practical implications. The proposed DSS framework can lead to improved building project outcomes, reduced environmental footprints, and more resilient infrastructure regarding energy consumption. Future Research Directions: further research endeavors should delve deeper into the practical implementation of the DSS framework across diverse engineering projects. Exploring variations of fuzzy-AI could further enhance the decision support system. Additionally, investigating the potential barriers and challenges in adopting this approach will be crucial in ensuring its widespread adoption.

Intelligent Decision Support System for Building Project Management Based on Artificial Intelligence

With the deepening of research on artificial intelligence (AI) technology in the field of computer science and the improvement of its social influence, the effective combination of AI technology and engineering project management practice has become a hot topic of discussion in the field of engineering project management. In the application research of AI in engineering project management, most of them use data-driven models to solve project management problems, improve data insight and improve the speed and quality of project management decision-making. Decision support systems have been developed on the basis of management information systems, but they have rarely been successfully applied in project management. The decision support system based on data warehouse is a new level of development in decision support systems, providing an effective way for projects to achieve real-time decision-making, thereby achieving synchronous, integrated, and optimized dynamic management of project progress, cost and function, and improving the timeliness and scientificity of decision-making. Therefore, this article proposes the theoretical framework and architecture of a construction project decision support system based on data warehousing. Then, this article proposes a deep belief network (DBN) evaluation method to evaluate the performance of the system, and the results show that the proposed method has superior performance.

Employing Fuzzy AHP in Modeling a Decision Support System for Determining Scholarship Recipients within the University Context

In the realm of university scholarship programs, the process of selecting deserving recipients presents a complex decision-making challenge. This study explores the integration of Fuzzy Analytical Hierarchy Process (AHP) into the modeling of a Decision Support System (DSS) aimed at facilitating the determination of scholarship awardees. The utilization of Fuzzy AHP enables a more comprehensive and nuanced evaluation of candidates by accommodating uncertainties and imprecisions inherent in the decision-making process. This research investigates the application of Fuzzy AHP within the specific context of university scholarship recipient selection. The proposed DSS framework not only enhances the objectivity and transparency of the decision-making process but also contributes to the optimization of resource allocation and the identification of candidates best aligned with the scholarship's objectives. By employing Fuzzy AHP in this decision-support context, universities can effectively address the intricate considerations involved in awarding scholarships, thereby promoting fairness and increasing the likelihood of rewarding the most deserving individuals.

Evaluating the blockchain-based healthcare supply chain using interval-valued Pythagorean fuzzy entropy-based decision support system

In the current era, blockchain technology has emerged as a novel technique to maintain the operations of healthcare management systems. The global COVID-19 disease has led to increase in the utilization of technology for healthcare supply chain, patient data management, and claims settlement. Data management in healthcare industry is a complex procedure where multiple organizations offer appropriate supply chain facilities in everyday life. Inadequate data handling disrupts the supply chain, which has a longstanding impact on the healthcare region. Blockchain-based solutions are renowned for their ability to produce safe and traceable results and effective in the health sector for secure data retrieval and storage, resulting in more effectual product creation and tracking. These technologies can deliver data provenance, sponsors candid healthcare industry demands, and certifies the immutability of multi direction transactions. Due to involvement of multiple factors/criteria, the selection of an effective blockchain platform-based healthcare supply chain can be treated as a decision support system (DSS) problem. Thus, the objective of this paper is to present a novel interval-valued Pythagorean fuzzy DSS for evaluating the blockchain platforms for healthcare supply chain and selecting a suitable one with respect to multiple criteria and uncertainty. The proposed DSS framework is divided into three stages. First, some fairly aggregation operators are presented to combine the individual’s information and also discussed their desirable characteristics on interval-valued Pythagorean fuzzy sets (IVPFSs). Second, an integrated weighting tool is developed using entropy-based tool for objective weight of criteria and pivot pairwise relative criteria importance assessment (PIPRECIA) model for subjective weight of criteria with IVPFSs. For this purpose, new entropy is presented on IVPFSs. Third, the multi-attribute ideal-real comparative analysis (MAIRCA) model is developed with the combination of fairly aggregation operators, entropy-based weighting tool and PIPRECIA model with interval-valued Pythagorean fuzzy information. Further, the proposed DSS is used on a case study of blockchain platforms for healthcare supply chain evaluation, which confirms its applicability and usefulness. Sensitivity and comparative assessments are carried out to check the consistency, robustness and efficiency of the presented method.

Integrated Decision Support Framework of Optimal Scaffolding System for Construction Projects

Selecting the appropriate temporary facilities is important for reducing cost and improving the productivity and safety of craft professionals in construction projects. However, the manual planning process for scaffolding systems is typically prone to inefficiencies. This paper aims to develop a knowledge-based framework for a scaffolding decision support system for industry. An integrated two-phase system was established, including a technical evaluation module and a knowledge-based module. First, the system identifies feasible scaffolding alternatives from the database through a rule-based algorithm. Second, a knowledge-based module was designed to assess the alternative performance. The framework effectively generated the ranking of scaffolding alternatives, and the top three influential factors were identified, including the site accessibility, protection to workers and health risk. Thus, an application study of an industrial steel project was proffered to validate the effectiveness of the framework. The proposed framework may help decision-making regarding the implementation of temporary facility planning in industry practices. It has wider applicability because it simultaneously considers site conditions, productivity, safety, and financial benefits, and is designed and implemented through a computerized path. The paper contributes to the industry by developing an integrated decision support system for temporary facilities. Additionally, the practical contribution of this research is the provision of an optimized scaffolding planning method that could be utilized as a guide when implementing the decision support system.

ID2S4FH: A Novel Framework of Intelligent Decision Support System for Fire Hazards

Modern societies and industrial sectors are serviced through storage and distribution centres (SDCs) such as supermarkets, malls, warehouses, etc. Large quantities of supplies are stocked here, e.g., food grains, clothes, shoes, pharmaceuticals, electronics, plastics, edible oils, electrical wires/equipment, petroleum products, painting materials, etc. Fires due to the burning of these materials are categorized into six classes, viz., Class A, Class B, Class C, Class D, Class K, and Class F. A fire is extinguished better when the right type of fire retardant is used. A thumb rule on firefighting also says, “never fight a fire if you do not know what is burning”. In this paper, we have proposed an Intelligent Decision Support System (ID2S4FH) to generate a real-time ‘fire-map’ of such SDCs during a fire hazard. We have interfaced six tin-oxide-based gas sensor elements, a temperature and humidity sensor, and a particulate matter (PM) sensor with microcontrollers to capture the real-time signature patterns of the ambient air. We burned sixteen different types of materials belonging to six classes of fire and created a dataset consisting of 2400 samples. The sensor array responses were then pre-processed and analysed using various classifiers trained in different analysis space domains. Among the classifiers, four classifiers achieved ‘all correct’ identification of the fire classes of 80 unknown test samples, and the lowest mean squared error (MSE) achieved was 2.81 × 10−3. During a fire hazard, our proposed ID2S4FH can generate real-time fire maps of SDCs and help firefighters to extinguish the fire using the appropriate fire retardant.

Hit or miss: A decision support system framework for signing new musical talent

In the music industry, the process of signing new musical talent is one of the most complex decision-making problems. The decision, which is generally made by an artist and repertoire (A&R) team, involves consideration of various quantitative and qualitative criteria, and usually results in a low success rate. We conducted a series of mental model interviews with the aim of developing a decision support framework for A&R teams. This framework was validated by creating a decision support system that utilises multi-criteria decision analysis to support decision-making. Our framework and subsequent implementation of the decision support system involving decision rule and weighted sum methods show an improvement in the ability to analyse and decide on greater amounts of talent. This paper serves as a building block for developing systems to aid in this complex decision-making problem.

A Framework of Spatial Decision Support System (SDSS) for Crime Investigations

Crime investigation have gained attention among multidisciplinary domain experts due to severity of impact to strategic decision making and crime management. Current study develops decision support tools by introducing spatial decision support system for investigating crime. Spatial analysis can be integrated into decision support system for robust analysis. Spatial decision support system framework comprises of four main components; (I) database, (ii) knowledge-base, (iii) user interface and (iv) knowledge base. Spatial analysis involve location-based point in order to identify crime distribution. SCRIVIS-DSS is divided into three modules; from pattern identification module to information visualization module followed by decision making module. Therefore, this effort could be served as basis to spatial component to assist decision makers in crime investigation.

Integrating the Digital Twin of Decision Support Systems in Aeronautics

A Decision Support System (DSS) is a computerized system that helps users make decisions. In the field of air transport, RAL has developed DSSs to support decision making in various settings, including surface transportation and national security. The purpose of this chapter is to explore the maintenance hypothesis of conditional status checking and propose supporting concepts, such as enhanced care and proactive maintenance. These concepts are further enhanced by robust validation and strategies to improve the effectiveness of care in an extraordinary way. Therefore, a decision support system framework is necessary to build today's flying maintenance framework. Maintenance Decision Support Systems (MDSS) offer a valuable tool for aeronautical support, enhancing maintenance efficiency and cost control. However, further research is required to delve into the associated topics of flying MDSS. Due to the diverse configurations and intricate structures of modern aircraft, a significant number of aircraft parameters need to be evaluated. To facilitate further research, it is essential to establish systematic procedures for compiling a valuable database and define research criteria to assess the sufficiency of existing information. Considering the cost issues associated with continuous flight position information, determining the criteria for data download should guide further research.

An interpretability security framework for intelligent decision support systems based on saliency map

An interpretability security framework for intelligent decision support systems based on saliency map

DAAR: Drift Adaption and Alternatives Ranking approach for interpretable clinical decision support systems

Diagnosing allergy comorbidities is challenging because the comprehensive studies on the prevalence of each allergy and the relation with other diseases are scarce. Moreover, there is a chance for uncertainty in the intradermal skin test results due to poor administration, manual observation and interpretation. Hence this work aims to develop a Decision Support System (DSS) for enhancing the diagnosis of allergy comorbidities. The DSS framework consists of a knowledge engineering phase and decision-making phase. The former focuses on detecting the concept drift, pre-processing the data, and extracting the knowledge, whereas, the latter focuses on the DAAR approach for adapting the concept drift and ranking the alternatives. The intradermal skin test data collected from allergy testing centre in India during 2012 and 2019 is used for developing and evaluating the DSS. The three techniques of concept drift adaption, namely, replacement, ensemble, and incremental are experimented on both balanced and unbalanced datasets. It can be observed from results that the incremental adaption technique has achieved an accuracy of 70.28% and 54.94% with complete decision, and 91.42% and 76.37% with partial decision on query data of 2012 and 2019 respectively. Moreover, the accuracy of the incremental technique is above the average of the accuracies of state-of-art machine learning algorithms. This implies that the framework is capable of supporting clinicians in choosing an optimal decision from the preference order of alternatives. Moreover, the interpretable knowledge provided by the system about all possible combinations of alternatives aids clinicians in understanding the reasoning behind their choice.

Rationalizing the Use of COVID-19 Questionable Efficacy Drugs in a Low Middle-income Country: A Hospital Tocilizumab Committee Experience.

Only corticosteroids have confirmed mortality benefits in coronavirus disease of 2019 (COVID-19). Rational use of costlier drugs with questionable benefits poses a great concern to hospital pharmacies in low middle-income countriesAim: The present study aimed to assess the rational utilization of hospital supply tocilizumab and understand its clinical benefits in hospitalized COVID-19 pneumonia patientsMethods: The Hospital Tocilizumab Committee (HTC) decision support system framework was developed to make patients eligible or ineligible for tocilizumab procurement from the hospital pharmacy. A total of 33 consecutive patients receiving tocilizumab were analyzed retrospectively in the 3-month study period. The records of the inpatient stay of the patients were observed for pulse, blood pressure, respiratory rate (RR), oxygen saturation (SpO2), fraction of inspired oxygen (FiO2) laboratory work-up, hospital stay duration, and mortality benefit, if any. Patients were analyzed as "died," "survived," and "composite" subgroupsResults: The study observed death as a final outcome in 48% of patients. The study observed a significant effect of tocilizumab on C-reactive protein (CRP) (p = 0.02) and ferritin (p = 0.018) levels on a 10-day follow-up when all patients were analyzed together. Rising and declining trends of RR and FiO2 were observed among the "died" (RR, p = 0.02; FiO2, p = 0.03) and survived (RR, p = 0.03; FiO2, p = 0.05) subgroups. The second dose of tocilizumab was received by 88% of survivors as against 50% of patients who died (p = 0.04)Conclusion: Hospital Tocilizumab Committee (HTC) was successfully established to continue the assessment of the costlier drug with uncertain treatment benefits. A repeat dose of tocilizumab may provide a mortality benefit in Asian Indians.

Design for assembly approach for additive manufacturing products: a decision support system for large-size AM products

In a contemporary era, Additive Manufacturing (AM), 3D printing or rapid prototyping has evolved as a distinctive method when compared with the traditional manufacturing. By addressing the topic of Design for Additive Manufacturing (DFAM), it is observed that the basic principles of DFAM and Design for Assembly (DFA) are well established and usually applicable on small-size AM parts. To address this critical manufacturing decision, our research work presents a new decision support system (DSS) for a large-size AM part which is based on compiling the existing DFAM methodologies. Before presenting the new DSS, the previous DFAM approaches are reviewed and investigated the research trends in part decomposition (PD), part consolidation (PC), and topology optimization (TO). The literature is categorized into six distinctive categories and among them the first phase is the information phase. Following this information requisite step, the next phase is parameter assessment phase and so on. The new DSS starts with the clarification of the design goal while in previous approaches this step was usually done at the later stages. Similarly, the remaining steps are efficiently integrated into the framework structure. The developed system is also guiding the post-decomposition assembly process. The developed DSS is validated using the case study of a 6-axis robotic arm. Moreover, a comprehensive concept for using the developed DSS framework is also presented in the research work.