Mathematical Models For Decision Support Research Articles

Coupling Wearable Devices and Decision Theory in the United States Emergency Department Triage Process: A Narrative Review.

This research was motivated by the nurses’ decision-making process in the current emergency department (ED) triage process in the United States. It explores how continuous vital signs monitoring can be integrated into the ED. The article presents four shortcomings on current ED triage systems and proposes a new conceptual clinical decision support model that exploits the benefits of combining wireless wearable devices with Multi-Attribute Utility Theory to address those shortcomings. A literature review was conducted using various engineering and medical research databases, analyzing current practices and identifying potential improvement opportunities. The results from the literature review show that advancements in wireless wearable devices provide opportunities to enhance current ED processes by monitoring patients while they wait after triage and, therefore, reduce the risk of an adverse event. A dynamic mathematical decision support model to prioritize patients is presented, creating a feedback loop in the ED. The coupling of wearable devices (to collect data) with decision theory (to synthesize and organize the information) can assist in reducing sources of uncertainty inherent to ED systems. The authors also address the feasibility of the proposed conceptual model.

МОДЕЛЬ ПІДТРИМКИ РІШЕНЬ ГРУПИ ІНВЕСТОРІВ ПРОЕКТІВ ДЛЯ SMARTCITY З УРАХУВАННЯМ БАГАТОФАКТОРНОСТІ

Creating a multi-factor decision support model for a group of investors for Smart City development. The article describes the method of using of the mathematical decision support model in the evaluation of investment projects. The results are based on the decision of a bilinear multi-step quality game with several terminal surfaces. The model is focused on further software implementation of the decision support system (DSS) or expert system for cross-platform software products. In doing so, players control a dynamic system in multidimensional project spaces. The scientific novelty of the article is that, in contrast to the existing solutions and close to the conceptual focus of research, in the article, for the first time we consider a new class of bilinear multiplayer games. For the first time, we have proposed a solution that allows us to appropriately and correctly describe the investment processes, considering the multifactorial nature of the problem. Computational experiments were performed in MatLab to find sets, investor preferences, and their optimal financial strategies in the analysis of Smart City development projects. The results of the computational experiments confirmed the correctness and capability of the model. In terms of practical importance, the scope of investing in Smart City development projects is considered, given the multifactorial nature of the task and the potential financial strategies of investor groups. The model makes it possible to evaluate the attractiveness for the investor groups (players) of the projects being analyzed.

Mathematical models for decision support in wildfires prevention with incomplete initial data

Mathematical models for decision support in wildfires prevention with incomplete initial data

On the practical identifiability of a two-parameter model of pulmonary gas exchange.

BackgroundSuccessful application of mechanical ventilation as a life-saving therapy implies appropriate ventilator settings. Decision making is based on clinicians’ knowledge, but can be enhanced by mathematical models that determine the individual patient state by calculating parameters that are not directly measurable. Evaluation of models may support the clinician to reach a defined treatment goal. Bedside applicability of mathematical models for decision support requires a robust identification of the model parameters with a minimum of measuring effort. The influence of appropriate data selection on the identification of a two-parameter model of pulmonary gas exchange was analyzed.MethodsThe model considers a shunt as well as ventilation-perfusion-mismatch to simulate a variety of pathologic pulmonary gas exchange states, i.e. different severities of pulmonary impairment. Synthetic patient data were generated by model simulation. To incorporate more realistic effects of measurement errors, the simulated data were corrupted with additive noise. In addition, real patient data retrieved from a patient data management system were used retrospectively to confirm the obtained findings. The model was identified to a wide range of different FiO2 settings. Just one single measurement was used for parameter identification. Subsequently prediction performance was obtained by comparing the identified model predicted oxygen level in arterial blood either to exact data taken from simulations or patients measurements.ResultsStructural identifiability of the model using one single measurement for the identification process could be demonstrated. Minimum prediction error of blood oxygenation depends on blood gas level at the time of system identification i.e. the measurement situation. For severe pulmonary impairment, higher FiO2 settings were required to achieve a better prediction capability compared to less impaired pulmonary states. Plausibility analysis with real patient data could confirm this finding.Discussion and conclusionsDependent on patients’ pulmonary state, the influence of ventilator settings (here FiO2) on model identification of the gas exchange model could be demonstrated. To maximize prediction accuracy i.e. to find the best individualized model with as few data as possible, best ranges of FiO2-settings for parameter identification were obtained. A less effort identification process, which depends on the pulmonary state, can be deduced from the results of this identifiability analysis.

Mathematical Models for Decision Support. NATO ASI Series F, Vol. 48

"Mathematical Models for Decision Support. NATO ASI Series F, Vol. 48." Journal of the Operational Research Society, 41(2), pp. 180–181

Mathematical Models for Decision Support. NATO ASI Series F, Vol. 48

Mathematical Models for Decision Support. NATO ASI Series F, Vol. 48

Mathematical models for decision support

It is quite an onerous task to edit the proceedings of a two week long institute with learned contributors from many parts of the world. All the same, the editorial team has found the process of refereeing and reviewing the contributions worthwhile and completing the volume has proven to be a satisfying task. In setting up the institute we had considered models and methods taken from a number of different disciplines. As a result the whole institute - preparing for it, attending it and editing the proceedings - proved to be an intense learning experience for us. Here I speak on behalf of the committee and the editorial team. By the time the institute took place, the papers were delivered and the delegates exchanged their views, the structure of the topics covered and their relative positioning appeared in a different light. In editing the volume I felt compelled to introduce a new structure in grouping the papers. The contents of this volume are organised in eight main sections set out below: 1 . Abstracts. 2. Review Paper. 3. Models with Multiple Criteria and Single or Multiple Decision Makers. 4. Use of Optimisation Models as Decision Support Tools. 5. Role of Information Systems in Decision Making: Database and Model Management Issues. 6. Methods of Artificial Intelligence in Decision Making: Intelligent Knowledge Based Systems. 7. Representation of Uncertainty in Mathematical Models and Knowledge Based Systems. 8. Mathematical Basis for Constructing Models and Model Validation.

Mathematical models for decision support

It is quite an onerous task to edit the proceedings of a two week long institute with learned contributors from many parts of the world. All the same, the editorial team has found the process of refereeing and reviewing the contributions worthwhile and completing the volume has proven to be a satisfying task. In setting up the institute we had considered models and methods taken from a number of different disciplines. As a result the whole institute - preparing for it, attending it and editing the proceedings - proved to be an intense learning experience for us. Here I speak on behalf of the committee and the editorial team. By the time the institute took place, the papers were delivered and the delegates exchanged their views, the structure of the topics covered and their relative positioning appeared in a different light. In editing the volume I felt compelled to introduce a new structure in grouping the papers. The contents of this volume are organised in eight main sections set out below: 1 . Abstracts. 2. Review Paper. 3. Models with Multiple Criteria and Single or Multiple Decision Makers. 4. Use of Optimisation Models as Decision Support Tools. 5. Role of Information Systems in Decision Making: Database and Model Management Issues. 6. Methods of Artificial Intelligence in Decision Making: Intelligent Knowledge Based Systems. 7. Representation of Uncertainty in Mathematical Models and Knowledge Based Systems. 8. Mathematical Basis for Constructing Models and Model Validation.

Mathematical models for decision support

Mathematical models for decision support