A fuzzy sets theory application in determining the severity of respiratory failure (original) (raw)
Related papers
2010
In the paper, a medical application in determining the severity of respiratory distress in a patient in an intensive care unit is considered in a simplified version, based on fuzzy sets theory. An overview of previous results is given, as well as directions for further development of the application. In that framework, known models of fuzzy multicriteria decision making are applied in two situations: the first, with all features of equal importance and without interaction between criteria, and the second, with interacting criteria. The practical usage of both approaches is shown. In the first case the Bellman-Zadeh's approach is used, and in the second case, the Choquet integral is used. The further development could include the model improvements dealing with interaction indices, as well as with the problem of identifying fuzzy measures. Some other directions for possible further work on the application considered are pointed out. I.
Improvements in the fuzzy model of determining the severity of respiratory failure
Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)
The object of the paper is the determination of the severity of respiratory distress of a patient in an intensive care unit using fuzzy sets theory. The determination of the degree of respiratory distress is of extreme clinical relevance. In [1] the proposition is given to treat the problem by the theory of fuzzy sets and the computer implementation of the model is considered. The problem is modeled as a fuzzy multicriteria decision-making one. In this paper the use of fuzzy measures, and of corresponding Choquet integral are considered in order to get improvement of results from [1]. The improvement is due to the fact that the application of fuzzy measures can take into account the interaction between the criteria.The results are given and the directions for possible further work are pointed out.
A fuzzy model of determining severity of respiratory distrees and possibilities of implementation
2004
The paper presents the research results which can be applied in medicine: the fuzzy model of determination of severity of respiratory distress in a patient in an intensive care unit, based on the usage of a kind of fuzzy aggregation operator, the Choquet integral. The determination of the degree of respiratory distress is of extreme clinical relevance. The approach proposed in the paper is the improvement of existing models, due to the fact that the application of fuzzy measures can take into account the interaction between criteria. Possibilities of Web-based model implementation are considered. The results are given and the directions for possible further work are pointed out.
Enhanced Decision Support Systems in Intensive Care Unit Based on Intuitionistic Fuzzy Sets
Advances in Fuzzy Systems
In areas of medical diagnosis and decision-making, several uncertainty and ambiguity shrouded situations are most often imposed. In this regard, one may well assume that intuitionistic fuzzy sets (IFS) should stand as a potent technique useful for demystifying associated with the real healthcare decision-making situations. To this end, we are developing a prototype model helpful for detecting the patients risk degree in Intensive Care Unit (ICU). Based on the intuitionistic fuzzy sets, dubbed Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS), the shown work has its origins in the Modified Early Warning Score (MEWS) standard. It is worth noting that the proposed prototype effectiveness validation is associated through a real case study test at the Polyclinic ESSALEMA cited in Sfax, Tunisia. This paper does actually provide some practical initial results concerning the system as carried out in real life situations. Indeed, the proposed system turns out to prove tha...
Controlling mechanical ventilation in acute respiratory distress syndrome with fuzzy logic
Journal of Critical Care, 2014
Purpose-The current ventilatory care goal for acute respiratory distress syndrome (ARDS), and the only evidence-based approach for managing ARDS, is to ventilate with a tidal volume (V T) of 6 ml/kg predicted body weight (PBW). However, it is not uncommon for some caregivers to feel inclined to deviate from this strategy for one reason or another. To accommodate this inclination in a rationalized manner, we previously developed an algorithm that allows for V T to depart from 6 ml/kg PBW based on physiological criteria. The goal of the present study was to test the feasibility of this algorithm in a small retrospective study. Materials and Methods-Current values of peak airway pressure (PAP), positive endexpiratory pressure (PEEP) and arterial oxygen saturation (SaO 2) are used in a fuzzy logic algorithm to decide how much V T should differ from 6 ml/kg PBW and how much PEEP should change from its current setting. We retrospectively tested the predictions of the algorithm against 26 cases of decision making in 17 patients with ARDS. Results-Differences between algorithm and physician V T decisions were within 2.5 ml/kg PBW except in 1 of 26 cases, and differences between PEEP decisions were within 2.5 cm H 2 O except in 3 of 26 cases. The algorithm was consistently more conservative than physicians in changing V T , but was slightly less conservative when changing PEEP. Conclusions-Within the limits imposed by a small retrospective study, we conclude that our fuzzy logic algorithm makes sensible decisions while at the same time keeping practice close to the current ventilatory care goal.
Multicriteria Decision Making by Fuzzy Relations and Weighting Functions for the Criteria
A multicriteria decision making problem with criteria giving fuzzy relations between the couples of alternatives is considered. The importance of each of the criteria is given as weighting function depending on the membership degrees of the corresponding fuzzy relation. Transformed membership degrees with the help of these weighting functions are used in the aggregation procedure for fusing the relations. The properties of the weighted relations, required to decide the problems of choice, ranking or clustering of the alternatives' set are proved. An illustrative numerical example solving the problem of alternatives' ranking is presented as well.
A Novel Fuzzy Logic Inference System for Decision Support in Weaning from Mechanical Ventilation
Journal of Medical Systems, 2009
Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors.
Background: Chronic Obstructive Pulmonary Disease (COPD) is the most common known complication of exposure to mustard gas. Thus, all clinical guidelines have provided some recommendation for diagnosis, clinical management and treatment of this disease. Decision support systems are used to increase the acceptance of clinical guidelines. The purpose of this research is to develop a CDSS to determine the severity of COPD in chemical injured victims. Objectives: Development of a decision support system to determine the severity of COPD. Patients and Methods: First, the variables influencing to determining the severity of the disease was classified through studying the clinical guidelines. Then, the fuzzy model was implemented. To testing the system, the data from 50 patients were used. Results: the overall accuracy in determining the severity of the injury is equal to 92%, these indicators reflect the proper functioning of the system to assist the physician regarding the diagnosis of chronic obstructive pulmonary disease and determining its severity. Conclusions: The CDSS has efficient results and satisfactory performance. Although, the medical expert systems cannot be expected to provide 100 percent correct responses, however, they can be useful in the areas of patient management, diagnosis and treatment planning.