Application Of Decision Support System Research Articles

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

Logical Platforms for Mobile Application in Decision Support Systems Based on Color Information Processing

A Systematic Review on Fuzzy Decision Support Systems and Multi-Criteria Analysis in Urban Heat Island Management

The phenomenon known as urban heat islands (UHIs) is becoming more common and widespread, especially in large cities and metropolises around the world. The main cause of these temperature variations between the city center and the suburbs is the replacement of large tracts of natural land with artificial (built-up) surfaces that absorb solar heat and radiate it back at night. UHIs have been the subject of numerous studies, most of which were about defining the main characteristics, factors, indexes, etc., of UHIs using remote sensing technologies or about determining mitigating activities. This paper provides a comprehensive overview of the literature, as well as a bibliometric analysis, to discover research trends related to the application of decision support systems and multi-criteria decision-making for UHI management, with a special emphasis on fuzzy theory. Data collection is conducted using the Scopus bibliographic database. Throughout the literature review, it was found that there were not many studies on multi-criteria analysis and decision support system applications regarding UHIs. The fuzzy theory application was also reviewed, resulting in only a few references. However, this topic is current, with an increase in published papers, and authors see this as an opportunity for improvement and further research.

INTELLIGENCE ANALYTICAL SUBSYSTEM OF CORPORATE MANAGEMENT (ON THE EXAMPLE OF AN OIL & GAS COMPANY)

The changes that have taken place in the Ukrainian economy in recent years due to the consistent impact of global macroeconomic, epidemiological and military factors of the current deep crisis - actualized for enterprises and corporations a complex adaptive reorganization of their analytical subsystem of their management [1, 2].
 In addition, in order to be consistently effective in today's global business environment (liberalization of markets, their globalization, increased competition, decreasing consumer loyalty, constant variation in oil and gas prices, etc.), oil and gas companies must have an innovative data and knowledge environment that allows seamless and effective joint leveraging knowledge inside the corporation and across the value chain [3]. The application of knowledge-based decision support systems as an important tool of innovative management policy enables effective and sustainable management of a complex of integrated processes of an oil and gas production company (mitigating the impact of crisis factors) [4].
 The scientific and practical results presented in the article regarding the methodology and design of innovative analytical subsystem of the corporation management information systems should be taken into account when reengineering corporate information systems not only in the oil and gas industry in Ukraine [5-6]. 
 Moreover, the obtained results are relevant and applicable not only for local companies, but also for international corporations on emerging markets in the context of global and regional macroeconomic and edipemical crisis phenomena.

GC-CDSS: Personalized gastric cancer treatment recommendations system based on knowledge graph

BackgroundGastric cancer (GC) is one of the most common malignant tumors in the world, posing a serious threat to human health. Currently, gastric cancer treatment strategies emphasize a multidisciplinary team (MDT) consultation approach. However, there are numerous treatment guidelines and insights from clinical trials. The application of AI-based Clinical Decision Support System (CDSS) in tumor diagnosis and screening is increasing rapidly. ObjectiveThe purpose of this study is to (1) summarize the treatment decision process for GC according to the treatment guidelines in China, and then create a knowledge graph (KG) for GC, (2) based on aforementioned KG, built a CDSS and conducted an initial feasibility evaluation for the current system. MethodsFirstly, we summarized the decision-making process for treatment of GC. Then, we extracted relevant decision nodes and relationships and utilized Neo4j to create the KG. After obtaining the initial node features for building the graph embedding model, graph embedding algorithm, such as Node2Vec and GraphSAGE, were used to construct the GC-CDSS. At last, a retrospective cohort study was used to compare the consistency between GC-CDSS and MDT in treatment decision making. ResultsIn current study, we introduce a GC-CDSS, which is constructed based on Chinese GC treatment guidelines knowledge graph (KG). In the KG, we define four types of nodes and four types of relationships, and it comprise a total of 207 nodes and 300 relationships. Regarding GC-CDSS, the system is capable of providing dynamic and personalized diagnostic and treatment recommendations based on the patient's condition. Furthermore, a retrospective cohort study is conducted to compare GC-CDSS recommendations with those of the MDT group, the overall consistency rate of treatment recommendations between the auxiliary decision system and MDT team is 92.96%. ConclusionsWe construct a GC treatment support system, GC-CDSS, based on KG. The GC-CDSS may help oncologists make treatment decisions more efficient and promote standardization in primary healthcare settings.

The Application of Knowledge-Based Clinical Decision Support Systems to Detect Antibiotic Allergy.

Prevention of drug allergies is important for patient safety. The objective of this study was to evaluate the outcomes of antibiotic allergy-checking clinical decision support system (CDSS), K-CDSTM. A retrospective chart review study was performed in 29 hospitals and antibiotic allergy alerts data were collected from May to August 2022. A total of 15,535 allergy alert cases from 1586 patients were reviewed. The most frequently prescribed antibiotics were cephalosporins (48.5%), and there were more alerts of potential cross-reactivity between beta-lactam antibiotics than between antibiotics with the same ingredients or of the same class. Regarding allergy symptoms, dermatological disorders were the most common (38.8%), followed by gastrointestinal disorders (28.4%). The 714 cases (4.5%) of immune system disorders included 222 cases of anaphylaxis and 61 cases of severe cutaneous adverse reactions. Alerts for severe symptoms were reported in 6.4% of all cases. This study confirmed that K-CDS can effectively detect antibiotic allergies and prevent the prescription of potentially allergy-causing antibiotics among patients with a history of antibiotic allergies. If K-CDS is expanded to medical institutions nationwide in the future, it can prevent an increase in allergy recurrence related to drug prescriptions through cloud-based allergy detection CDSSs.

Decision Making System For Selection Of Prospective Scholarship Recipients Using The Saw (Simple Additive Waighting) Method At Vocational School Bina Negara Gubug

The selection system at Bina Negara Gubug Vocational School Jl. KH. Hasan Anwar No.9 Gubug currently processes data on the criteria for each student for each type of scholarship. It does not yet have a database system but uses a computerized system with Microsoft Excel, so there are often delays in the selection process in preparing the selection report for scholarship recipients. This research uses the Research and Development (R & D) development model by Borg and Gall with 6 steps of development, namely Research and Information Collecting, Planning, Develop Premilinary Form of Product, Premilinary Field Testing, Main Product Revision, Main Field Testing. The scholarship selection decision support system application product uses the SAW (Simple Additive Waighting) method. Visual Basic 6.0 development software and Microsoft Access database. This system can provide a useful solution for the decision-making system for selecting scholarship recipients for schools so that a better and faster selection can be achieved.

Web-Based Employee Performance Assessment Application with SMART Method: An Agile Approach in the Context of the Smartphone Market

Employee performance is essential in determining the company's sustainability, so company leaders must pay attention to employee performance. Many subjective factors determine employee performance, so performance evaluations are sometimes unfair. One of the companies developing in Indonesia is a smartphone sales company, CV Jala Kencana. As the company develops and the number of employees increases, problems arise in assessing employee performance that have yet to be measurable. So that employee performance assessments can be more objective, a decision support system application is needed that can help assess employee performance. The Agile SDLC method is used in designing website-based employee performance assessment applications. Then, for the employee performance assessment feature, the Simple Multi-Attribute Rating Technique (SMART) method is used, and SHA512 encryption is applied to the employee performance assessment database, which cannot be manipulated. This research produces a web-based employee performance assessment application with features to assess employee performance objectively based on predetermined criteria.

A web-based microbiological hazard identification tool for infant foods

An integrated approach to identify and assess Microbiological Hazards (MHs) and mitigate risks in infant food chains is crucial to ensure safe foods for infants and young children.A systematic procedure was developed to identify MHs in specific infant foods. This includes five major steps: 1) relevant hazard-food pairing, 2) process inactivation efficiency, 3) recontamination possibility after processing, 4) MHs growth opportunity, and 5) MHs-food association level. These steps were integrated into an online tool called the Microbiological Hazards IDentification (MiID) decision support system (DSS), targeting food companies, governmental agencies and academia users, and is accessible at https://foodmicrobiologywur.shinyapps.io/Microbial_hazards_ID/. The MiID DSS was validated in four case studies, focussing on infant formula, fruit puree, cereal-based meals, and fresh fruits, each representing distinct products and processing characteristics. The results obtained through the application of the MiID DSS, compared with identification by food safety experts, consistently identified the top MHs in these food products. This process affirms its effectiveness in systematic hazard identification.The introduction of the MiID DSS helps to structure the first steps in HACCP (hazard analysis) and in risk assessment (hazard identification) to follow a structured and well-documented procedure, balancing the risk of overlooking relevant MHs or including too many irrelevant MHs. It is a valuable addition to risk analysis/assessment in infant food chains and has the potential for future extension. This includes the incorporation of newly acquired data related to infant foods via a semi-publicly hosted platform, or it can be adapted for hazard identification in general food products using a similar framework.

Application of DSS in agroecological assessment of the potential of use of chernozem soils in the conditions of the Penza region

The paper discusses the use of a decision support system (DSS) for the purpose of automated agroecological assessment of chernozem soils of the Penza region in comparison with the agroecological classification of soil cover based on standard agrochemical characteristics. The results show the rationality of using DSS, as well as the increased agroecological potential of the analyzed old arable field. Calculations made it possible to identify limiting soil-ecological parameters, such as insufficient content of mobile forms of phosphorus and the density of the arable soil horizon, as well as to calculate the soil-ecological quality rating both for the entire field and for a regular hectare grid.

Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Decision support system applications (1980-2022). Classification and statistical analysis

Decision support system applications (1980-2022). Classification and statistical analysis

Smart Medicine: Exploring the Landscape of AI-Enhanced Clinical Decision Support Systems

A Clinical Decision Support System (CDSS) combines medical knowledge with patient data to help healthcare providers make well-informed decisions. It offers real-time advice and recommendations for better patient outcomes and treatment management. CDSS enhances clinical decision-making by analysing information, identifying patterns, and offering evidence-based insights at the point of care. This abstract delves into the realm of Smart Medicine, investigating the application of AI-enhanced Clinical Decision Support Systems (CDSS) through the utilization of two prominent Convolutional Neural Network (CNN) architectures—VGGNet and ResNet. The study explores the landscape of these advanced systems in the healthcare domain, emphasizing the role of VGGNet's simplicity and transfer learning capabilities, and ResNet's innovative approach to addressing the challenges of training deep networks. The research scrutinizes their efficacy in capturing intricate medical patterns, offering insights into the nuanced decision-making processes within clinical settings. By navigating the landscape of AI-driven CDSS, this study contributes to the ongoing dialogue on optimizing healthcare outcomes through the integration of sophisticated neural network architectures. The findings shed light on the potential benefits and considerations associated with VGGNet and ResNet in shaping the future of AI-enhanced clinical decision support in Smart Medicine.

Clinical decision support system for clinical nurses’ decision-making on nurse-to-patient assignment: a scoping review protocol

IntroductionOptimal nurse-to-patient assignment plays a crucial role in healthcare delivery, with direct implications for patient outcomes and the workloads of nursing staff. However, this process is highly intricate, involving a...

Design and Implementation of Smart Teaching for Computer Courses in Colleges and Universities Enabled by Artificial Intelligence Technology

Abstract In this study, an intelligent decision support system for education was successfully constructed by integrating data mining techniques, evaluation similarity matrix and novel braided net clustering method. Based on the evaluation similarity matrix, this system brings together decision-making members with similar views through clustering analysis. It calculates these categorized groups’ preferences, consistency indexes, and decision values to derive the optimal solution. The research in this paper empirically validates the effectiveness of the intelligent decision support system against the background of actual teaching scenarios and with all students in their classes as the research subjects. The validation results show that in the Powerpoint Practical and PS Practical tests, the average scores of the students in the experimental class reached 87.1 and 86.2, respectively, and this score is significantly higher than that of the control class. In addition, in the Analysis of the four dimensions of pre-course learning behavior (including interest in learning, attitude toward learning, etc.), the mean values of the experimental class (4.46, 3.99, 3.89, 4.18) were also significantly higher than those of the control class (3.2, 2.73, 2.94, 2.89). The application of the Intelligent Decision Support System increased students’ learning interest and effectively improved their performance in both machine and written tests. The main effect of its teaching method is significant with an F-value of 18.029 and a P-value of less than 0.001. The results of this study confirm the practical application of intelligent decision support systems in teaching and learning, and provide a new methodology and perspective for computer-intelligent teaching.

Trends and Futuristic Applications of Big Data and Electronic Health  Record Data in Empowering Constructive Clinical Decision Support  Systems

Modern healthcare benefits significantly from big data, electronic  healthcare record data technologies, and artificial intelligence, which  provide robust tools for the collection and analysis of vast and diverse  datasets originating from various sources, such as clinical care,  administration, and research. This advancement enables the creation  of information technology infrastructures that facilitate the  realization of the "Learning Healthcare System Cycle," wherein  healthcare practice and research seamlessly intertwine in a  synergistic manner. This review focuses on illustrating how the  integration of extensive data collections, empowered by big data, can  enhance clinical decision-making and advance biomedical research.  Most importantly, electronic health records offer several benefits,  including heightened accessibility to patient information, enhanced  interdisciplinary communication, improved continuity of care, legible documentation, minimized duplication, and increased efficiency. The  incorporation of computerized physician order entry within electronic  medical records contributes to patient safety by mitigating medication  errors and offering clinical guidance through prompts and alerts  during electronic order entry. Furthermore, when evidence-based  clinical decision support is integrated with electronic health records,  it serves as a valuable tool for guiding healthcare providers and  clinicians in aligning their clinical practices with meaningful use  standards and compliance with quality metrics. This review also  outlines the contemporary application of clinical decision support  systems in the field of medicine. Its types, their existing practical  applications with documented effectiveness, prevalent challenges, and  potential adverse consequences. The review concludes by offering  evidence-based guidelines aimed at mitigating risks associated with  support systems design, implementation, evaluation, and ongoing  maintenance. 

Decision Support System for Selecting Tofu Dregs Suppliers Using the Analytical Hierarchy Process (AHP) Method

The process of selecting suppliers can be simplified with the aid of a system that offers recommendations for optimal supplier choices. This research aims to develop a decision support system application to assist CV. TJIGOWEK in objectively selecting tofu dregs suppliers while enhancing effectiveness and efficiency in time management. The research methodology employed to facilitate supplier selection is the Analytical Hierarchy Process (AHP). The five fundamental criteria utilized as parameters include quality, quantity, pricing, service, and payment flexibility. The AHP method is employed to determine the weight of each criterion, ultimately generating the optimal alternative ranking. The findings of this study can serve as a valuable reference for making supplier selection decisions aligned with the company's predefined criteria.

Application of Decision Support System in Determining the Best Physics Learning Media Aids Using VIKOR, EDAS, and EXPROM II Methods

The development of effective physics learning media is a major challenge in supporting an innovative and result-oriented learning process. This research aims to implement a Decision Support System (DSS) in the selection of physics learning media aids by using the EDAS, VIKOR, and EXPROM II methods. The EDAS method is used to evaluate alternative learning media tools based on tiered preferences, while VIKOR provides an optimal solution by considering the best and worst values. In addition, EXPROM II provides an explicit assessment of complexity and risk in the context of physics learning media selection. The practical implications of these findings can help decision makers in the field of education to improve the quality of physics learning media used in the educational process

Application of Electre Method Decision Support System for Laptop Selection

In the contemporary era, laptops have transitioned from being perceived as luxury items to becoming an essential tool for work, education, and various learning activities. The market is saturated with a plethora of laptop manufacturers, providing consumers with a wide array of options to cater to their specific needs and budget constraints. However, a common challenge faced by consumers is the mismatch between their requirements and the specifications of available laptops, often due to a lack of understanding. Addressing this issue requires the implementation of a suitable Decision Support System (DSS). The proposed solution involves employing the Electre method, renowned for its multicriteria approach. This method systematically eliminates unsuitable alternatives, facilitating the identification of laptops that align with the desired criteria. The process involves several key steps, such as defining criteria and alternatives, assigning weights, normalizing the decision matrix, determining concordance and discordance indices, and ultimately establishing an aggregate dominance matrix. By leveraging the Electre method, this Decision Support System can effectively recommend laptops that not only meet the consumer's specified criteria but also align with their budget constraints. This systematic approach aims to enhance the overall decision-making process, ensuring optimal satisfaction for laptop buyers in terms of both performance and financial considerations.

Recent advances in multi-criteria decision analysis: A comprehensive review of applications and trends

In recent years, multi-criteria decision support methods have become widely used research tools by both scientists and practitioners. Theoretical works involving new multi-criteria methods and developments of existing methods immediately find applications in areas of business practice. This paper attempts to sort out the areas of application of MCDA methods. For this purpose, the relevant literature from 2018–2023 was reviewed. The inclusion selection criteria were defined as addressing multi-criteria practical issues, open accessibility, comprehensive research methodology and findings, and the use of an MCDA method in the evaluation process. Detailed research identified that the main areas of use of MCDA methods are healthcare, energy management, supplier selection, and transportation. Due to the timeliness and importance of the problem, a detailed study of the use of MCDA methods in sustainability issues was separated. The paper contributes contributions to both theory and economic practice. The article provides a series of recommendations both on the methodological side of the problems to be solved and shows the practical prism of individual decision models, offering ready-to-use decision models, and providing a comprehensive review of recent MCDA advancements, fostering informed and reliable decision-making. Additionally, this review sheds light on the latest developments in MCDA, emphasizing trends in Decision Support Systems (DSS) and prominent application areas in contemporary research.

A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial.

Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant. The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior. This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907). The recruitment process started in May 2023 and is expected to conclude on December 2023. The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer. ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181. DERR1-10.2196/48852.