A fuzzy model of determining severity of respiratory distrees and possibilities of implementation (original) (raw)
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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.
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.
A fuzzy sets theory application in determining the severity of respiratory failure
International Journal of Medical Informatics, 2001
This paper proposes to apply the theory of fuzzy sets in determining the severity of respiratory distress of a patient in an intensive care unit. The problem is modelled as a fuzzy multicriteria decision-making one. Theoretical approaches to the situation are considered. The proposed fuzzy selection method, based on the usage of the fuzzy intervals, is described. An example of determining the severity of respiratory distress is used to illustrate the presentation. Computer implementation of the model is considered, and the directions for possible further work are pointed out.
Evaluation of Pulmonary Function Tests by Using Fuzzy Logic Theory
Journal of Medical Systems, 2009
Pulmonary Function Tests (PFTs) are very important in the medical evaluation of patients suffering from "shortness of breath", and they are effectively used for the diagnosis of pulmonary diseases, such as COPD (i.e. chronic obstructive pulmonary diseases). Measurement of Forced Vital Capacity (FVC) and Forced Expiratory Flow in the 1st second (FEV1) are very important for controlling the treatment of COPD. During PFTs, some difficulties are encountered which complicate the comparison of produced graphs with the standards. These mainly include the reluctance of the patients to cooperate and the physicians' weaknesses to make healthy interpretations. Main tools of the diagnostic process are the symptoms, laboratory tests or measurements and the medical history of the patient. However, quite frequently, most of the medical information obtained from the patient is uncertain, exaggerated or ignored, incomplete or inconsistent. Fuzziness encountered during PFT is very important. In this study, the purpose is to use "fuzzy logic" approach to facilitate reliable and fast interpretation of PFT graphical outputs. A comparison is made between this approach and methodologies adopted in previous studies. Mathematical models and their coefficients for the spirometric plots are introduced as fuzzy numbers. Firstly, a set of rules for categorizing coefficients of mathematical models obtained. Then, a fuzzy rule-base for a medical inference engine is constructed and a diagnostic "expert system COPDes" designed. This program, COPDes helps for diagnosing the degree of COPD for the patient under test.
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.
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.
Estimation of the pulmonary elastance and setting of the ventilation condition using fuzzy logic
Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications, 2014
Artificial respirators are widely used for patients with little or no autonomous breathing ability. Doctors are required to pay scrupulous attention for the use of the artificial respirators. And doctors must set the artificial respirator in consideration of each patient's pulmonary characteristic. However, we do not understand the pulmonary characteristic of the patient by the measurement of data. Therefore, the setting of the artificial respirator is decided by the experience and the intuition of the doctor now. Purpose of this study are to develop a method to estimate the static P − V curve and the pulmonary elastance of the patient and to set a ventilation condition of the artificial respirator. The static P − V curve and the pulmonary elastance expresses the important feature of the lung, and the static P − V curve is a basis for deciding the airway pressure limit value. In our previous work, we have presented an estimation technique of the pulmonary elastance by fuzzy logic. Parameters of the pulmonary elastance (f E (V)) are different in each fuzzy rules. Then, it is said that other parameters do not change in a short time(one cycle breath). Nevertheless, in the previous study, these parameters were estimated to be different values in parameters estimation of each fuzzy rule. It is considered that the estimated precision of the static P − V curve is influenced by these values. We solve this problem using new estimation procedure. In addition, a ventilation condition of the artificial respirator is set using estimated static P − V curve.
Probabilistic fuzzy prediction of mortality in intensive care units
2012
In the present work, we propose the application of probabilistic fuzzy systems (PFS) to model the prediction of mortality in septic shock patients. This technique is characterized by the combination of the linguistic description of the system with the statistical properties of data. Preliminary results for this particular clinical problem point that PFS models, besides performing as accurately as first order Takagi-Sugeno fuzzy models, also provide probability measures that provide additional clinical information upon which physicians can act on.
Modelo fuzzy estimando tempo de internação por doenças cardiovasculares
Ciência & Saúde Coletiva, 2015
A fuzzy linguistic model based on the Mamdani method with input variables, particulate matter, sulfur dioxide, temperature and wind obtained from CETESB with two membership functions each was built to predict the average hospitalization time due to cardiovascular diseases related to exposure to air pollutants in São José dos Campos in the State of São Paulo in 2009. The output variable is the average length of hospitalization obtained from DATASUS with six membership functions. The average time given by the model was compared to actual data using lags of 0 to 4 days. This model was built using the Matlab v. 7.5 fuzzy toolbox. Its accuracy was assessed with the ROC curve. Hospitalizations with a mean time of 7.9 days (SD = 4.9) were recorded in 1119 cases. The data provided revealed a significant correlation with the actual data according to the lags of 0 to 4 days. The pollutant that showed the greatest accuracy was sulfur dioxide. This model can be used as the basis of a specialize...
The fuzzy medical group in the centre for computational Intelligence
Artificial Intelligence in Medicine, 2001
In this paper, ®ve ongoing or completed research projects in medicine using fuzzy sets and logic are summarized. They are, a lightweight fuzzy process for diagnosis using fuzzy symptoms, prediction of pulmonary embolisms from linguistic descriptions of perfusion and ventilation scans, application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classi®cation, the development of a fuzzy expert system for the analysis of umbilical cord blood, modeling nursing intuition using type 2 fuzzy sets. These projects use a variety of fuzzy methods including clustering, simple set aggregation and type 2 inferencing to achieve their aims. The ongoing research projects re¯ect an interest in using type 2 fuzzy sets for dealing with vagueness and linguistic knowledge which is commonly found in medical areas where perceptions rather than measurements are the norm. # (P.R. Innocent). 0933-3657/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 3 3 -3 6 5 7 ( 0 0 ) 0 0 0 8 1 -6