Development of a fuzzy decision support system to determine the severity of obstructive pulmonary in chemical injured victims

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. © 2015 Taha Samad-Soltani, Mostafa Ghanei, Mostafa Langarizadeh

Comparing predictions made by a prediction model, clinical score, and physicians Pediatric asthma exacerbations in the emergency department

Background: Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. Objectives: First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. Design: A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2, data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Measurements: Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. Results: In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant. Conclusion: Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

A fuzzy rule-based expert system for asthma severity identification in emergency department

The emergency department (ED) of a hospital is an important unit that deals with time-sensitive and life-threatening medical cases. Rapid treatment and accuracy in diagnosis are considered the main characteristics of excellent operational processes in ED. However, in reality, long waiting time and uncertainty in the diagnosis has affected the quality of ED services. Nonetheless, these problems can be improved by utilising computing technologies that assist medical professionals to make fast and accurate decisions. This paper investigates the issues of under-treatment and uncertainty condition of acute asthma cases in ED. A novel approach, known as the fuzzy logic principle is employed to determine the severity of acute asthma. The fuzzy set theory, known as Fuzzy Rule-based Expert System for Asthma Severity (FRESAS) determination is embedded into the expert system (ES) to assess the severity of asthma among patients in ED. The proposed fuzzy methodology effectively manages the fuzziness of the patient’s information data, and determines the subjective judgment of medical practitioners’ level on eight criteria assessed in severity determination. Knowledge acquisition and representation, fuzzification, fuzzy inference engine, and defuzzification are the processes tested by the FRESAS development that incorporates expert advice. The system evaluation is performed by using datasets that were extracted from the ED clerking notes from one of the hospitals in Northern Peninsular Malaysia.System evaluation demonstrates that the proposed system performs efficiently in determining the severity of acute asthma. Furthermore, the proposed system offers opportunities for further research on other types of diseases in ED, and improves other hybridisation approaches

Case-Based-Reasoning System for Feature Selection and Diagnosing Disease; Case Study: Asthma

Asthma is a chronic informatory disease of the respiratory canals in which it has not become obvious what is the reason for the reports argumentation on the ground of asthma prevalence. In the present research, the purpose would be to design a case-based-reasoning (CBR) model in order to assist a physician to diagnose the type of disease and also the needed therapy. At first for designing this system, the disease variables were discriminated and were at the patients' disposal as a questionnaire, and after gathering the relevant data (CBR) algorithm was rendered on the data which led to the asthma diagnosis. The system was tested on 325 asthmatic and non asthmatic adult cases and was accessed with eighty percent accuracy. The consequences were promising. With regard to the fact that the factors of the disease are different in various countries, This study was performed in order to determine risk factors for asthma in Iranian society and the results of research showed that the most important variables of asthma disease in Iran are symptoms heperresponsivity, frequency of cough, cough. Key words: data mining, case based reasoning, asthma, diagnosis

Analysis of Respiratory Sounds: State of the Art

Objective This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. Methods and material Review of the current medical and technological literature using Pubmed and personal experience. Results The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… Conclusion The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools