Decision Support Information Research Articles

The use of fuzzy sets to form the staffing of a medical institution

The timely provision of medical care directly depends on the well-planned time of the medical staff. The use of information systems and decision support systems can improve efficiency and reduce healthcare costs. A difficult problem to solve in medical information systems is the uncertainty of information associated with the failure to meet the conditions for completeness and reliability of incoming information when organizing the work of a medical institution. The use of a fuzzy logic mechanism in decision support systems to assess the staffing of a medical institution will allow taking into account the influence of many factors at the initial decision-making stage, which will contribute to improving the efficiency of the personnel of a medical organization.

Effects of Personalized Recommendation System Based on User Learning Behavior in English-Chinese Translation

Users may receive personalised information services and decision support from personalised recommendations. In this paper, a hybrid algorithm-based personalised recommendation approach for learning English is proposed. The user model is created by merging user interest tags, and the Person Rank algorithm is then recommended based on user information. Second, the question-and-answer model is created once the question-and-answer data has been labelled, and the Problem Rank algorithm is suggested in accordance with the question-and-answer data. Then, the approach of tag-based recommendation, comparable user recommendation, and multi-dimensional sliding window are used to construct the recommendation algorithm model. The experimental findings demonstrate that, following the model’s training with the gradient descent technique, the recommendation accuracy is steady at around 0.78, the suggested information can accommodate users who are learning English, and the personalised recommendation effect is enhanced.

SmartFarm: A Machine Learning-Based Analytical System for Soil Classification and Crop Recommendation in Precision Agriculture

Abstract: SmartFarm is an innovative machine learning-driven analytical system designed to revolutionize precision agriculture through accurate soil classification and tailored crop recommendations. The project focuses on collecting comprehensive soil and historical crop data, employing rigorous preprocessing techniques, and implementing knowledge-based classification and non-parametric classifiers such as decision trees and neural networks. The system integrates these models, ensuring a cohesive approach to support seamless decision-making for farmers. With a user-friendly interface, SmartFarm enables farmers to input soil data and receive personalized crop recommendations. The project's scalability, adaptability, and commitment to iterative improvement through user feedback make it a promising solution for enhancing agricultural productivity and sustainability. The outcomes contribute to the evolving landscape of precision agriculture, emphasizing the power of machine learning in informed decision support systems. The outcomes underscore the potential of machine learning in fostering informed decision-making and sustainable farming in the evolving landscape of precision agriculture

Business intelligence and the leverage of information in healthcare organizations from a managerial perspective: a systematic literature review and research agenda

PurposeBusiness intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations’ (HCOs) services are offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.Design/methodology/approachA literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.FindingsIn total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research’s stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.Originality/valueTo the authors’ knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.

Research of the optimization problem of structure hierarchical communication network with changing its parameters

The article is devoted to the study of the problem of optimizing the hierarchical structure of a multicommodity communication network with discrete flows when changing its important parameters, such as the capacity of network arcs in transport blocks and the size of the transport block for transportation of the discrete small-lot cargo or of the data transmission in a digital communication network. The network has three levels of hierarchy – a backbone, a zonal and an internal and four types of nodes - backbone nodes of the first, second and third types, forming the backbone and zonal levels of the network, and nodes of the fourth type, which subordinate to each backbone node and forming the internal levels of the network. Types of nodes differ from each other in terms of functionality. The main task of the study is to establish how the structure of the backbone network changes (the number and location of backbone nodes of the first, second, and third types), the flow processing and distribution scheme, and the technical and economic indicators of the network's functioning for different values of its parameters. The principles of organization of sorting and distribution of flows in a three-level network and its mathematical model are given. A mathematical model of the optimization problem of the backbone network structure and flow sorting and distribution scheme is formulated. Algorithms for solving the problem are based on the discrete analogue of the local descent method proposed by the authors earlier, when the neighborhoods of the metric space of possible solutions are chosen based on heuristic considerations, taking into account the specifics of the problem being solved. Computer modeling of the problem on a network containing 10 nodes at the specified change in the both parameters is carried out. The modeling was carried out on the example of the transport network of cargo transportation using a computer program that is part of the instrumental software of the Information and Analytical Decision Support System (IA DSS), which is being developed at the Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine. An experimental study of solution the problem showed that the structure of the network is weakly dependent on the change in the carrying capacity of the arcs and the size of the transport block. The proposed computer technology for solving problem when the network parameters are changed allows you to interactively modeling various options of a network, changing the topology, hierarchical structure, flows, parameters and constraints of the model and from the set of the obtained solutions to choose a best option, taking into account the selected a goal function and the accepted constraints; calculate preliminary technical and economic indicators of the network's functioning, estimate the cost of additional resources and plan the amount of investment required for the modernization and construction of its structural elements, which ultimately makes it possible to increase the efficiency functioning of the network by optimizing use of its resources and reducing the operating costs for the processing and transportation of flows.

Development of climate informed management scenarios for fisheries in the eastern Bering Sea

Abstract In recognition of the impact of climate change on marine ecosystems worldwide, integrated research teams have coupled climate change projections with social-ecological models to inform management and evaluate adaptation strategies for the fishing industry and fishing communities. A key step in this process is the selection of scenarios that enable improved adaptation strategies and decision-making through engagements with constituents with diverse interests in the future use of marine resources. This paper presents an approach to selecting and refining climate-informed social-ecological scenarios for groundfish and crab fisheries in the eastern Bering Sea. The approach involved: (a) initial model development to provide worked examples; (b) engagement with stakeholders to seek input on climate-related concerns, priorities, and adaptation options; and (c) establishment of pathways for uptake of climate-informed decision support information into existing management systems. This approach narrowed the range of candidate scenarios, identified pressing climate concerns of constituents, and clarified timelines for scheduling modeling projects to address these concerns. Separating evaluation of management strategies (research modeling) from proposed changes to Fishery Management Plans preserved opportunities for public debate of proposed changes through a well-established regulatory review process. Collectively, these outcomes help to advance the development of a regionally relevant climate-ready harvest policy.

Поиск множества допустимых управленческих решений в технических системах

Scientific and technological progress leads to a continuous increase in the number and complexity of designed systems, as a result of which there is a need to create modern information and intelligent decision support systems. Such systems will make it possible to predict various scenarios of management decisions, which in turn helps to increase the efficiency and quality of the project. Effective use of automation in the search for management decisions can reduce the time spent on analysis, improve the quality of decisions made and increase the overall productivity and competitiveness of the organization. The purpose of this work is to substantiate the scientific approach to the issue of automated search for acceptable management solutions in technical systems. To describe management decisions, a concept is proposed that allows one to compactly depict the structure of complex hierarchical systems. Many solutions are described, and the problem of finding acceptable management solutions is formulated. A system for automated search for management decisions is described. An algorithm is presented for searching solutions that are feasible with respect to a certain condition from a set of possible ones, based on removing the vertices of the search tree that are invalid according to the specification. A description of the automation of building a model of many technical solutions is presented. Using a model to search for a set of acceptable management decisions allows you to more accurately predict various scenarios for the management decision made and helps to increase the efficiency and improve the quality of the project.

Design of a Web-Based Decision Support Information System for YMM FI Scholarship Recipients Using the SMART Method

Designing a Web-Based YMM FI Scholarship Recipient Decision Support Information System Using the SMART Method can help YMM FI in the decision-making process to determine the right scholarship recipient. YMM FI is a zakat fund management foundation which has several programs, one of which is still running, the Edu Care Program which focuses on funding education from the lower to upper levels. However, the selection of prospective recipients of higher education scholarships is still done manually, which results in inaccuracies in those who receive them. For this reason, there is a need for a Web-Based Decision Support System using the SMART Method which takes into account several attributes that have weighted values and criteria. From the results found, attribute A2 with the name Wa Marlin is the highest ranking of the other attributes

Artificial Intelligence in Medicine

Artificial Intelligence (AI) is the technology related to simulating human behaviour in machines. Machine intelligence is a subfield of AI in which available raw data is processed to learn inherent patterns and build a model to adapt to new data. Deep learning models utilize very large amount of data and extract important features and classify the data. Multiagent systems or distributed AI systems are autonomous, proactive, reactive and have ability to interact with humans and other agents. Medicine includes all the processes involved in preventing, diagnosing and curing diseases. It includes medical staff and supporting staff records, drug information, decision support information for medical professionals, clinical lab tests, X-rays, magnetic resonance images, surgeries, and so on. AI has a number of applications in medicine including expert systems, medical robots, medial image analysis and distributed medical agents etc. Expert systems can function as medical experts and helpful for patients who are unable to reach a medical specialist due to cost, or being in a remote area. The role of AI is significant in radiology as abnormal data is labelled in medical images obtained from computed tomography, X-rays, and magnetic resonance imaging etc. more accurately. Medical robots assist in patient care, clinical settings, surgeries and in many other ways. Distributed medical agents enable the availability of a number of medical experts online to examine critical cases. In this paper the role of artificial intelligence in the above mentioned medical applications is elaborated with relevant examples. It is concluded that AI is indispensable in medicine for effective and efficient healthcare.

Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction

Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Trás-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.

State of the Art on the Contributions and Use of Geographic Information Systems (GIS) in Epidemiology in Morocco

Background The Geographic Information System (GIS) plays a significant role in the healthcare information system. The use of GIS as an epidemiological research tool is very little known in Morocco. According to the Moroccan Ministry of Health's Health Plan for 2025, axis 23 aims to complete the deployment of the decision support information system through the implementation of the geographic information system with the goal of improving governance and optimizing resource allocation. This creates constraints in achieving the design of an appropriate GIS for modeling pathologies using remote sensing and spatial statistical methods. The current work aims to understand GIS and its applications in epidemiology, with a focus on the state of scientific knowledge related to GIS in epidemiological research. Methods A state-of-the-art review of the various uses of GIS in epidemiological research was conducted using several university databases, such as PubMed and Scopus, recent articles, and certain reference books, focusing on the following keywords: “utilization,” “GIS,” and “epidemiology,” as well as their synonyms identified by MeSH. Results The majority of GIS research applied to epidemiology primarily involves making observations using GIS, which are then incorporated into action plans to promote population health. Therefore, it is important to break down the aspects of GIS (Geographic Information System) usage in epidemiology into four components. These include epidemiological surveillance, determining primary healthcare needs, healthcare planning, and healthcare quality. Conclusion These results clearly demonstrate the significant contribution of GIS to the improvement of public health. However, there is a need for positive action regarding the integration of this system, especially in developing countries.

Electronic health record and primary care physician self-reported quality of care: a multilevel study in China

ABSTRACT Background Health information technology is one of the building blocks of a high-performing health system. However, the evidence regarding the influence of an electronic health record (EHR) on the quality of care remains mixed, especially in low- and middle-income countries. Objective This study examines the association between greater EHR functionality and primary care physician self-reported quality of care. Methods A total of 224 primary care physicians from 38 community health centres (CHCs) in four large Chinese cities participated in a cross-sectional survey to assess CHC care quality. Each CHC director scored their CHC’s EHR functionality on the availability of ten typical features covering health information, data, results management, patient access, and clinical decision support. Data analysis utilised hierarchical linear modelling. Results The availability of five EHR features was positively associated with physician self-reported clinical quality: share records online with providers outside the practice (β = 0.276, p = 0.04), access records online by the patient (β = 0.325, p = 0.04), alert provider of potential prescription problems (β = 0.353, p = 0.04), send the patient reminders for care (β = 0.419, p = 0.003), and list patients by diagnosis or health risk (β = 0.282, p = 0.04). However, no association was found between specific features availability or total features score and physician self-reported preventive quality. Conclusions This study provides evidence that the availability of EHR systems, and specific features of these systems, was positively associated with physician self-reported quality of care in these 38 CHCs. Future longitudinal studies focused on standardised quality metrics, and designed to control known confounding variables, will further inform quality improvement efforts in primary care.

A Prioritization Method to Optimize School Operational Assistance Fund by Using AHP Approach

In Indonesia, education funding provided by the central government is made up of the components of personnel costs, investment costs, and operating costs. Since its implementation in 2005, the program has found several obstacles. It’s crucial to have an optimization tool to overcome the complications. The main problems that have arisen in the allocation of these funds so far have been lacking in planning and the lack of participation of the school ecosystem, in particular in determining the planning, planning documents and the priority scale of the projects. Program and activity plans based on the School Operating Assistance (BOS) platform. The Analytical Hierarchy Process (AHP) model is a prioritization approach based on determining the priority scale on the comparison of one option with another. The AHP model is a powerful but simple method that is usually used to make decisions. This approach has been implemented in various sectors, business, government, management, and education. In this study, the AHP model was used to determine the priority scale on the criteria of BOS allocated funds in the current year so that it could be absorbed optimally and can be utilized properly in its implementation for Vocational School activities. The results of the study show that the use of AHP makes it easier for school residents to prioritize the use of funds for school activities. Using the AHP model allows managers to record strategic goals using weighted criteria as the basis for prioritizing the use of funds. AHP can also work among different stakeholders in the school (principals, treasurers, teachers, and committee, parents/guardians) to participate in decisions about the use of BOS funds, to improve the quality and form of decision support taken to improve school performance.

Systematic approach to providing intelligent decision support whilst digital transformation of management processes in agro-industrial complex

Modern economic conditions, characterized by digitalization processes and the influence of geopolitical factors contributing to the development of domestic information technologies have made the issue of providing support for management decision-making by specialized systems important. In this regard, the purpose of the study is to analyze the logistics of information flows and identify problems associated with the digital transformation of management processes in the agro-industrial complex (AIC). A systematic approach was chosen as the main method. The logical analysis and synthesis were used as well. Based on the identified movements and interrelations of information flows, and limiting factors for its effective use, an automated information and analytical decision support system (AIASDSS) is proposed in the context of a unified digital platform. Its components are described and principles of operation are formulated. In the focus is the creation of an information platform module oriented mainly on agricultural producers who occupy a central place in the agro-industrial complex. It is noted that work is underway to ensure the intellectualization of decision-making processes in agricultural organizations through the use of mathematical, statistical and information tools. Particular solutions are given. The research materials are addressed to authorities and can be used in managing agricultural production.

GastroBot: a Chinese gastrointestinal disease chatbot based on the retrieval-augmented generation.

Large Language Models (LLMs) play a crucial role in clinical information processing, showcasing robust generalization across diverse language tasks. However, existing LLMs, despite their significance, lack optimization for clinical applications, presenting challenges in terms of illusions and interpretability. The Retrieval-Augmented Generation (RAG) model addresses these issues by providing sources for answer generation, thereby reducing errors. This study explores the application of RAG technology in clinical gastroenterology to enhance knowledge generation on gastrointestinal diseases. We fine-tuned the embedding model using a corpus consisting of 25 guidelines on gastrointestinal diseases. The fine-tuned model exhibited an 18% improvement in hit rate compared to its base model, gte-base-zh. Moreover, it outperformed OpenAI's Embedding model by 20%. Employing the RAG framework with the llama-index, we developed a Chinese gastroenterology chatbot named "GastroBot," which significantly improves answer accuracy and contextual relevance, minimizing errors and the risk of disseminating misleading information. When evaluating GastroBot using the RAGAS framework, we observed a context recall rate of 95%. The faithfulness to the source, stands at 93.73%. The relevance of answers exhibits a strong correlation, reaching 92.28%. These findings highlight the effectiveness of GastroBot in providing accurate and contextually relevant information about gastrointestinal diseases. During manual assessment of GastroBot, in comparison with other models, our GastroBot model delivers a substantial amount of valuable knowledge while ensuring the completeness and consistency of the results. Research findings suggest that incorporating the RAG method into clinical gastroenterology can enhance the accuracy and reliability of large language models. Serving as a practical implementation of this method, GastroBot has demonstrated significant enhancements in contextual comprehension and response quality. Continued exploration and refinement of the model are poised to drive forward clinical information processing and decision support in the gastroenterology field.

Research on Information Management and Decision Support System Based on Big Data

Research on Information Management and Decision Support System Based on Big Data

Multi-dimensional analysis of financial accounting information and construction of decision support systems

Abstract Financial information is the core information of modern enterprises, which is of great significance to the sustainable development of enterprises. Based on the cost variance accounting model, the comprehensive budget management model, and the financial crisis early warning model, the financial information decision support system was constructed. Company S was selected for case analysis, and three modules in the decision-making system were used to analyze the company’s operating costs, overall budget management, and financial situation. Among the seven work centers of Company S, the consumption costs of the semi-finished product center and the procurement center account for the highest proportion, reaching more than 50% of the total manufacturing cost. Acquisition operations, raw material processing operations, semi-finished product operations, packaging operations, and quality inspection operations are some of the value-added operations of The Company. The operation efficiency of the procurement center, quality inspection center, maintenance center, and storage center is not high. Raw material processing centers, semi-finished product centers, and packaging centers are highly efficient. In 2022, the net profit of Company S will be -1.7592 million yuan, and the total profit will be -2.7418 million yuan. Management and selling expenses are exceeding the budget. The probability of financial risk was 82.35% and 88.46%, respectively. The company is facing serious financial risks due to its declining corporate sales, solvency, difficult capital turnover, and severe financial risks.

Spatiotemporal variation of habitat quality and its response to fractional vegetation cover change and human disturbance in the Loess Plateau

It is of great practical significance to regional ecological conservation and restoration to explore the spatiotemporal variation characteristics of habitat quality in the ecologically fragile Loess Plateau. This study firstly explored the habitat quality in the Loess Plateau during 2000-2020 with the Integrated Valuation of Ecosystem Services and Trade-offs model. Then this study revealed the response characteristics of habitat quality to the fractional vegetation cover (FVC) change and human disturbance with the geographically weighted regression (GWR) model. Results showed habitat quality tended to improve in 51.16% of the study area, and area of high or very high habitat quality increased by 1.78%. Besides, FVC showed dominantly significant increase (62.42%) and high stability (69.66%) in the study area, and human disturbance increased remarkably in 18.11% of the study area but maintained the same level in 91.83% of the study area. Additionally, areas with positive correlation between habitat quality change and FVC and between habitat quality change and human disturbance change accounted for 52.56% and 37.38% of the study area, respectively, indicating FVC played dominant role in affecting the regional habitat quality variation. This study can provide important decision support information for the future ecological conservation of the Loess Plateau.

An Information Service Platform for Decision Support in Academic Admissions Using Data Fabrics and Artificial Intelligence

This paper presents the architecture of an information service platform for decision support in academic admissions, utilizing data fabrics and artificial intelligence. The factors affecting students’ further education can be classified into four main types: (1) the course, (2) image, (3) personal reasoning of the student, and (4) public relations. The process of providing information for decision support in academic admissions can be divided into six stages: (1) collecting information, (2) matching study guidance, (3) recommending appropriate education, (4) confirming information, (5) assessing student admissions, and (6) providing feedback. Data fabric is an increasingly popular technology application for data management. The data fabric architecture consists of six layers: (1) an augmented data catalog, (2) a knowledge graph enriched with semantics, (3) metadata activation, (4) a recommendation engine for active metadata, (5) data preparation and integration, and (6) orchestration and data operations. A smart decision support system (DSS) technology is used to assist in decision-making. The results showed that this architecture has an excellent level of suitability (mean = 4.56, standard deviation = 0.35). It can be applied to a university to help it become a digital university and align with its mission.

Insights from field phenotyping improve satellite remote sensing based in-season estimation of winter wheat growth and phenology

Timely knowledge of phenological development and crop growth is pivotal for evidence-based decision making in agriculture. We propose a near real-time approach combining radiative transfer model inversion with physiological and phenological priors from multi-year field phenotyping. Our approach allows to retrieve Green Leaf Area Index (GLAI), Canopy Chlorophyll Content (CCC) and hence Leaf Chlorophyll Content (Cab) from Sentinel-2 optical satellite imagery to quantify winter wheat growth conditions in a physiologically sound way. Phenological macro stages are based on accumulated growing degree day thresholds obtained from multi-year field phenotyping covering more than 2400 ratings from roughly 300 winter wheat varieties and reflect important physiological transitions. These include the transition from vegetative to reproductive growth and the onset of flowering, which is important information for agricultural decision support. Validation against a large data set of on-farm trials in Switzerland collected in 2019 and 2022 revealed high accuracy of our approach that produced spatio-temporally consistent results. Phenological macro stages were predicted for 970 Sentinel-2 observations reaching a weighted F1-score of 0.96. Sentinel-2 derived GLAI and CCC explained between 77 to 84% and between 79 to 84% of the variability in in-situ measurements, respectively. Here, the incorporation of phenological priors clearly increased trait retrieval accuracy. Besides, this work highlights that physiological priors, e.g., obtained by field phenotyping, can help enhancing landscape scale observations and hold potential to advance the retrieval of remotely sensed vegetation traits and in-season phenology.