Design and implementation of a decision support system for flood management

Residential medical digital technology is an emerging discipline combining computer network technology and medical research. Based on the idea of knowledge discovery, this study was designed to construct a decision support system for remote medical management, analyze the need for utilization rate calculations and obtain relevant modeling elements for system design. Specifically, the model constructs a design method for a decision support system for the healthcare management of elderly residents through the use of a utilization rate modeling method based on digital information extraction. In the simulation process, the utilization rate modeling and system design intent analysis are combined to obtain the relevant functions and morphological characteristics that are essential to the system. Using regular usage slices, a higher-precision non-uniform rational B-spline (NURBS) usage rate can be fitted and a surface model with better continuity can be constructed. The experimental results show that the deviation of the NURBS usage rate generated by the boundary division from the original data model can reach test accuracies of 83, 87 and 89%, respectively. It is shown that the method can effectively reduce the modeling error caused by the irregular feature model in the process of modeling the utilization rate of digital information, and that it can ensure the accuracy of the model.

A prototype of knowledge-based decision support system for construction cost management

Design patterns for emergency management: An exercise in reflective practice

Exploiting the PAIN OUT registry with a clinical decision support system for acute pain management

Design of a computerized decision support system for hatchery production management

Within the framework of a technical cooperation project between ACSAD and BGR a Decision Support System (DSS) for water resources management was developed and applied in two pilot areas.

Industrial dairy farms boast highly advanced health monitoring and disease diagnosis systems. But without easily accessible, user-friendly web platforms for real-time decision-making, most dairy farmers cannot proactively manage herd health management and optimize treatments based on disease prediction and prevention. To bridge this gap, we have developed a web application of a Decision support system (DSS) for dairy health management based on machine learning. The system architecture combines a Flask backend with a React frontend and scalable cloud data storage and includes preprocessing, data integration, predictive modelling, and cost analysis. DSS forecasts herd diseases with an accuracy 6.66 mean absolute error and 2.35 median absolute deviation across predictions. Its core predictive capabilities rely on long short-term memory (LSTM) neural networks to forecast disease progression from historical records and on a linear trend model to project cuts in treatment costs. The system calculates medication dosages and cost per disease, streamlines supplier selection, and simulates various treatment scenarios, thereby identifying high-cost diseases with potential savings. In other words, this DSS application processes disease and treatment data by incorporating veterinary records into advanced data analytics and neural networks, thereby predicting diseases, optimizing disease prevention and treatment strategies, and reducing costs. As such, this DSS application provides dairy farmers with a tool for strategic decision-making, veterinary treatment planning, and cost-effective disease management towards improving animal welfare and increasing milk yield.

This article presents a new rule-based decision support system for critical infrastructure management based on the interdependencies among critical infrastructures. Unlike the conventional flood front prediction model, the new failure front prediction model consists of both physical parameters and interdependencies among infrastructures, which is represented by a multi-level complex network. This study employs advanced computation methods to build tools for better understanding emergent behavior of water security infrastructure systems for use in developing superior preparedness and response strategies. This study highlights the important role played by the interdependencies among the infrastructures in failure front prediction.

Neonatal jaundice is treated daily at all hospitals. However, the routine, urgency, and case load of most doctors stop them from carefully analyzing all the factors that they would like to (and should) take into account. This article develops a complex decision support system for neonatal jaundice management. The problem is represented by means of an influence diagram, including admission and treatment decisions. The corresponding uncertainty model is built with the aid of both historical data and subjective judgments. Parents and doctors were interviewed to elicit a multiattribute utility function. The decision analysis cycle is completed with sensitivity analyses and explanations of the results. The construction and use of this decision support system for jaundice management have induced a profound change in daily medical practice, avoiding aggressive treatments-there have been no exchange transfusions in the past 3 years-and reducing the lengths of stay at the hospital. More information is now taken into account to decide on treatments. Interestingly, after embarking on this modeling effort, physicians came to view jaundice as a much more difficult problem than they had initially thought. Comparisons between real cases and system proposals revealed that treatments by nonexpert doctors tend to be longer than what expert doctors would administer. The system is especially designed to help neonatologists in situations in which their lack of experience may lead to unnecessary treatments. Different points of view from several expert doctors and, more interestingly, from parents are taken into account. This knowledge gives a broader picture of the medical problem- incorporating new action criteria, new agents to intervene, more uncertainty variables-to get an insight into the suitability of each therapeutic decision for each patient situation. The benefits gained and the usefulness perceived by neonatologists are worth the increased and time-consuming effort of developing this complex system. Although specially designed for a specific hospital and for neonatal jaundice management, it can be easily adapted to other hospitals and problems.

An integrated expert decision support system for agricultural management

Decision Support System for Network Security Management is a system that evaluates the security of a network domain consists of various components and that supports a security manager in making decisions about security management of the network based on the evaluation. It helps the security manager to make a decision about how to change the configuration of the network to prevent the attack due to the security vulnerabilities of the network. Decision Support System for Network Security Management checks the current status of the network, predicts the possible intrusion and supports decision-making about security management to prevent the intrusion in advance. In this paper we analyze the requirements of the Decision Support System for Network Security Management that automates the security evaluation of the network and that supports decision-making about security management to secure the network, and we propose a design for it that satisfies the requirements. We also provide a prototype that implements the basic functions of our design.

Urban infrastructure assets (e.g. roads, water pipes) perform critical functions to the health and well-being of society. Although it has been widely recognised that different infrastructure assets are highly interconnected, infrastructure management in practice such as planning, installation and maintenance are often undertaken by different stakeholders without considering these dependencies due to the lack of relevant data and cross-domain knowledge, which may cause unexpected cascading social, economic and environmental effects. In this paper, we present a knowledge based decision support system for urban infrastructure inter-asset management. By considering various infrastructure assets (e.g. road, ground, cable), triggers (e.g. pipe leaking) and potential consequences (e.g. traffic disruption) as a holistic system, we model each sub-domain using a modular ontology and encapsulate the interdependence between them using a set of rules. Moreover, qualitative likelihood is assigned to each rule by domain experts (e.g. civil engineers) to encode the uncertainty of knowledge, and an inference engine is applied to predict the potential consequences of a given trigger with location specific data and the encoded rules. A web-based prototype system has been developed based on the above concept and demonstrated to a wide range of stakeholders. The system can assist in the process of decision making by aiding data collation and integration, as well as presenting potential consequences of possible triggers, advising on whether additional information is needed or suggesting ways of obtaining such information. The work shows an intelligent approach to integrate and process multi-source data to pioneer a novel way to aid a complex decision process with a high social impact.

Prostate cancer trajectory-map: clinical decision support system for prognosis management of radical prostatectomy.

ISHS International Symposium on Precision Management of Orchards and Vineyards Using stomatal conductance as a water status indicator in grapevine: implementation into a decision support system for irrigation management

Precision Agriculture(PA) is the modern Agriculture base on the 3S technologies and agricultural knowledge. The management and decision support system for PA is the core of PA. In order to resolve the problem of the acquirement, management, and analysis for data, and making intelligent decision support in PA, the system is integrated with Global Position System, Geographic Information System, Expert System and Decision Support System. From the system, Users can get not only the information of field, but also the measure scheme about variety selection, fertilization, prevention and control of plant diseases and elimination of pests. So that users can manage the farm in variable, reduce the cost and the pollution of environment and increase the income. The system is base on C/S, which is of stability and extensibility, and can be maintained and used easily. The system has been applied in the demonstration base of Precision Agriculture in NingXia Province.