Purpose Of Decision Support Systems Research Articles

Technical Requirement of Clinical Decision Support system for Diabetic Patients

Introduction: Diabetes is a public health problem which is originating an increment in the demand for health services. There is an obvious gap exists between actual clinical practice and optimal patient care, Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management. The purpose of this article is reviewing diabetes decision support systems based on system design metrics, type and purpose of decision support systems. Materials and Methods: The literature search was performed in peer reviewed journals indexed in PubMed by keywords such as medical decision making, clinical decision support systems, Reminder systems, diabetes, interface, interaction, information to 2019. This article review the diabetes decision support systems based on system design metrics (interface, interaction, and information), type and purpose of decision support system. Results: 32 of the 35 articles were decision support systems that provided specific warnings, reminders, a set of physician guidelines, or other recommendations for direct action. The most important decisions of the systems were support for blood glucose control and insulin dose adjustment, as well as 13 warning and reminder articles. Of the 35 articles, there were 21 user interface items (such as simplicity, readability, font sizes and ect), 23 interaction items (such as Fit, use selection tools, facilitate ease of use and ect. ) and 31 item information items (such as Content guidance, diagnostic support and concise and ect ).Discussion: This study identified important aspects of designing decision support system, It can be applied not only to diabetic patients but also to other decision support systems.Conclusion: Most decision support systems take into account a number of design criteria; system designers can look at design aspects to improve the efficiency of these systems. Decision support system evaluation models can also be added to the factors under consideration.

MODEL OF A FUZZY DYNAMIC DECISION SUPPORT SYSTEM

Within the framework of this article, the analysis of the ways of building an automated decision support system for business and technological processes based on the thematic apparatus of the theory of fuzzy functions is carried out. The purpose of decision support systems is to help the decision maker (business owners, top managers of companies, heads of state organizations and law enforcement agencies) in choosing the most optimal solution based on the information at his disposal. It is proposed to use mathematical methods based onfuzzy multicriteria ranking as methods for making a decision. The paper proposes a model of an automated decision support system for business and technological processes of organizations based onthe mathematical apparatus of the theory of fuzzy functions, which allows automating the process of choosing a solution and eliminating the influence of subjective factors when assessing possible decisions.

Modeling of road traffic noise and traffic flow measures to reduce noise exposure in Antalya metropolitan municipality.

BackgroundRoad traffic noise influencing directly public health in the modern cities is a growing problem in both developing and developed countries. The objective of this study was to model traffic-induced noise in Antalya province, validate the model with noise emission data, and to run the model for the noise preventive scenarios.MethodsIn this study, modeling of traffic-induced noise was performed using SoundPLAN® software at Gazi Boulevard in the city of Antalya. Calculations were made according to NMPB-Routes 96, which have been accepted by environmental noise legislation of the European Union and Turkey. Fundamental data sets such as geographical, topographical and meteorological data, building information and population, traffic network, traffic volume and vehicle speed, and composition of types of vehicle were utilized for the development of noise prediction model. Eight preventive scenarios to reduce traffic-induced noise levels were simulated using the validated model considering traffic flow measures such as types of vehicles, vehicle speeds, types of road surface, redirecting portion of heavy vehicles to alternative routes and noise barrier usage.ResultsResults showed that increase in heavy vehicle speeds in smooth road surface conditions caused more increase in exposures than that of light vehicle speed. It was highlighted that it would be appropriate to use porous road surface to reduce exposures on population on high-speed roads. Furthermore, the number of people that are exposed to noise is significantly reduced by precautions such as alternative routes for heavy vehicles and speed restriction. These precautions reduced noise exposures by 25.5–63.8%. The results showed that the usage of noise barrier at the alternative routes in case of porous asphalt road reduced population, dwellings, and area exposed to traffic noise which is greater than 75 dB(A) as 63.8, 40.5, and 60.0%, respectively.ConclusionIt could be concluded that the outcomes of the noise prediction models based on the generated scenarios could be used for the purpose of decision support system and could be helpful for decision-makers on the noise legislations.

Conceptualization of a framework of decision support system for agriculture in hilly region

Decision support system (DSS) in agriculture helps farming community to take appropriate decision as per the situation to maximize economic return by enhancing productivity and reducing the cost of inputs. The prime most purpose of DSS is to enhance the input use efficiency by applying the input when it is needed most. The requirement of DSS in the hilly states is being felt more as environmental conditions vary greatly in tempo-spatial domain. Climate change associated with increasing probability of extreme weather conditions has further deepened the need of DSS. There have been many attempts in the past to use / develop DSS in the hilly regions. The serious efforts in this direction were initiated by fine tuning the Decision Support System for Agrotechnology Transfer (DSSAT) in Indian conditions. DSSAT helps to take appropriate decisions on selection of cultivar, sowing time, irrigation, fertilization and harvesting of crops. Of late geospatial technology alone and in combination with crop simulation model has also been used to develop DSS. Present paper underlines the efforts of researchers / academicians to develop DSS in hilly states with their usability and limitations. Paper also conceptualizes a framework of DSS for hilly regions by integrating a forewarning system and agriculture expert system.

Decision Support as Knowledge Creation

The primary purpose of decision support systems (DSS) is to improve the quality of decisions. Since decisions are based on an individual’s mental model, improving decision quality is a function of discovering the decision maker’s mental model, and updating and/or enhancing it with new knowledge; that is, the purpose of decision support is knowledge creation. This article suggests that BI techniques can be applied to knowledge creation as an enabling technology. Specifically, the authors propose a business intelligence design theory for DSS as knowledge creation, a prescriptive theory based on Nonaka’s knowledge spiral that indicates how BI can be focused internally on the decision maker to discover and enhance his/her mental model and improve the quality of decisions.

Enhancing User Understanding in a Decision Support System: A Theoretical Basis and Framework

:The primary purpose of decision support systems (DSS) is to help the decision maker develop an understanding of the ill-structured, complex environment represented by the model. This paper concentrates on understanding the modeled environment through model analysis. Specifically, the purpose of this paper is to propose a framework for model analysis based on Perkins’s theory of understanding and its basic premise (knowledge as design) and basic components (purpose, models, and arguments). This framework encourages enhanced user understanding in a DSS via the synergistic combination and integration of: (1) cognitive science (theory of understanding), (2) artificial intelligence (machine learning, knowledge extraction, and expert systems), (3) model analysis (deductive and inductive), and (4) DSS (model management, instance management, and knowledge-base management).