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

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income

Perception of mathematics game’s design for primary school: based on teachers’ opinions

Unmistakable methods can be used for learning, and they can be looked at in a few viewpoints, particularly those identified with learning results. In this paper, we introduce an examination with a specific end goal to think about the design adequacy and development’s requirement of a game based learning (GBL) approach that is about to be used in LINUS screening for mathematics subject in primary school. The approach includes multiple interaction forms regarding addition and subtraction operation in mathematics based on LINUS constructs. Ten teachers from three different school located in Batu Pahat have participated in the study. The investigations involving survey activity by using questionnaire as the instrument. While breaking down the results, the outcomes demonstrated that the kids observed the amusement to be all the more fulfilling if there are less levels and more colours. Since the survey were conducted to a very common type of school in Malaysia, we believe game that is about to be built based on opinion gained could be utilized as an effective instrument in primary schools to strengthen pupils' lessons

Severity classification for idiopathic pulmonary fibrosis by using fuzzy logic

OBJECTIVE: To set out a severity classification for idiopathic pulmonary fibrosis (IPF) based on the interaction of pulmonary function parameters with high resolution computed tomography (CT) findings. INTRODUCTION: Despite the contribution of functional and radiological methods in the study of IPF, there are few classification proposals for the disease based on these examinations. METHODS: A cross-sectional study was carried out, in which 41 non-smoking patients with IPF were evaluated. The following high resolution CT findings were quantified using a semi-quantitative scoring system: reticular abnormality, honeycombing and ground-glass opacity. The functional variables were measured by spirometry, forced oscillation technique, helium dilution method, as well as the single-breath method of diffusing capacity of carbon monoxide. With the interaction between functional indexes and high resolution CT scores through fuzzy logic, a classification for IPF has been built. RESULTS: Out of 41 patients studied, 26 were male and 15 female, with a mean age of 70.8 years. Volume measurements were the variables which showed the best interaction with the disease extension on high resolution CT, while the forced vital capacity showed the lowest estimative errors in comparison to total lung capacity. A classification for IPF was suggested based on the 95% confidence interval of the forced vital capacity %: mild group (>92.7); moderately mild (76.9-92.6); moderate (64.3-76.8%); moderately severe (47.1-64.2); severe (24.3-47.0); and very severe (<24.3). CONCLUSION: Through fuzzy logic, an IPF classification was built based on forced vital capacity measurement with a simple practical application

A fuzzy knowledge based system for clinical diagnosis of tropical fever

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Sıtma ve tifo Sahra-altı Afrika'nın en büyük tropikal ateş enfeksiyonlarıdır. Her ikisi de bölgenin hastalık, ölüm ve ekonomik kayıplarının sebebidir. Tifo ateşi sebebiyle, her 100.000 kişiden 725 tifo vakasına yakalanmakta ve bu hastalardan da 7 adedi ölümle sonuçlandığı tahmin edilmektedir ve Dünya'nın sıtma ölümlerinin %90'ı Sahra-altı Afrika'da meydana gelmektedir. Bu iki hastalığın teşhisinde önemli olan çok sayıda belirti bulunması ve birçoğunun da ortak olması dolayısıyla teşhis zorlaşmaktadır. Bulanık küme teorisine ve insan gibi sonuçlandırma üzerine dayanan bulunak mantık, insani bilimlerde yaygın olarak kullanılmakta ve birçok problemi başarılı bir şekilde çözmektedir. Sınıflandırma ve karar verme görevlerine ihtiyaç duyulan tıbbi teşhis bu cazip uygulamalardan biridir. Belirsizliklerin olduğu teşhis özelliklerindeki karmaşıklıklar bilgisayar sistemlerinde kullanılan doğal dil ile üstesinden gelinmiştir. Bu çalışmada, Sahra-altı Afrika'da sıtma ve tifo ateşinin klinik teşhisi için bilgi tabanlı teşhis sisteminin (TROPFEV) tasarımında bulanık mantık kullanımı anlatılmaktadır. Bilgiler, tıp uzmanları danışmanlığında Uganda Sağlıklı Bakanlığı tarafından hazırlanan UCG-2012'den (Uganda Klinik Klavuzu 2012) çıkarım yapılmıştır. Bu kaynaklardan edinilmiş bilgiler modellenip, bulanık kural tabanlı mantık kullanılarak tanımlanmış ve Matlab 2012a gerçeklenmiştir. Toplanan bilgilere göre, 21 adet teşhis özellikleri, ateş hastalığının durumuna ya da şiddetine göre sistemi oluşturmak için düzenlenmiştir. Kullanıcı, karmaşık-sıtma, karmaşık olmayan-sıtma, karmaşık-tifo, karmaşık olmayan-tifo veya bilinmeyen ateş cevabını sistemden beklemektedir. Test ve performansını değerlendirmek için, TROPFEV sistemin sonuçları ile doktor tarafından yapılan teşhis sonuçlarıyla karşılaştırılmıştır. Uzman teşhisleri ve sistem teşhisleri arasındaki % 86 oranında doğruluk olduğunu görülmüştür. Sonuç olarak, tıbbi teşhis için tecrübesiz hekimlerin teşhislerine daha hızlı ve verimli bir şekilde teşhis koyabilmek için yardımcı olması amacıyla bulanık mantık kullanımına ağırlık verilebilir.. Çünkü bulanık mantık belirtilerdeki kesin olmama sıkıntılarının üstesinden gelebilmek için bulanıklık kümelerini kullanır ve bir sınıflandırmaya ilişkilendirir.Malaria and typhoid fever are major tropical fever infections. Both are responsible for significant morbidity, mortality and economic loss in the region. Typhoid fever is estimated to cause 725 incident cases and 7 deaths per 100,000 people in the year and on the other side 90% of the total world malaria deaths occur in the Sub-Saharan Africa. The two diseases malaria and typhoid fever have several diagnosis features with overlapping signs and symptoms which are a task in medical diagnosis. Fuzzy logic that lies on the fuzzy set theory and similar to human reasoning is widely used for human-related sciences, and successfully solves many problems. Medical diagnosis is one of these attractive applications, which requires classification and decision making tasks. It uses natural language to represent data into computer systems where complications in diagnosis features such as vagueness are perfectly handled. This thesis describes the use of fuzzy logic to design a knowledge based system for clinical diagnosis of malaria and typhoid fever (TROPFEV) in Sub-Saharan Africa. Knowledge was extracted from the documentary of UCG-2012 (Uganda Clinical Guidelines 2012) prepared by the ministry of healthy in Uganda as well as consulting medical experts. The knowledge acquired from these resources is modelled, represented using fuzzy rule based reasoning and implemented in Matlab 2012 a. According to the collected knowledge, 21 diagnosis features have been organised with their situations or severity during fever infections to build the system. The user is expected to get the answer of complicated malaria, uncomplicated malaria, complicated typhoid, uncomplicated typhoid or unknown fever. For testing and evaluating its performance, the results of the TROPFEV system were compared with the results of diagnosis made by a real doctor The difference in results between expert diagnosis and system diagnosis showed that the expert system have similarity with the real experts with 86% accuracy. In conclusion, the use of fuzzy logic in medical diagnosis can be emphasized because it provides an efficient way to assist inexperienced physicians to arrive at the final diagnosis of fever more quickly and efficiently. This is because fuzzy logic applies fuzzy sets to handle vagueness existing in symptoms

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

Design and conceptual proposal of an intelligent clinical decision support system for the diagnosis of suspicious obstructive sleep apnea patients from health profile

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient’s health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8–0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.Xunta de Galicia | Ref. ED481A-2020/03

Development of a fuzzy qualitative risk assessment model applied to construction industry

Dissertação para obtenção do Grau de Doutor em Engenharia IndustrialThe construction industry is plagued by occupational risky situations and poor working conditions. Risk Assessment for Occupational Safety (RAOS) is the first and key step to achieve adequate safety levels, particularly to support decision-making in safety programs. Most construction safety efforts are applied informally under the premise that simply allocating more resources to safety management will improve safety on site. Moreover, there are many traditional methods to address RAOS, but few have been adapted and validated for use in the construction industry, thus producing poor results. The contribution of this dissertation is a qualitative fuzzy RAOS model, tailored for the construction industry, named QRAM (Qualitative Risk Assessment Model). QRAM is based on four dimensions: Safety Climate Adequacy, (work accidents) Severity Factors, (work accidents) Possibility Factors and Safety Barriers Effectiveness. The risk assessment is based on real data collected by observation of reality, interviews with workers, foreman and engineers and consultation of site documents (working procedures, reports of work accident investigation, etc.), avoiding the use of data obtained by statistical tecnhiques. To rating each parameter it was defined qualitative evaluators - linguistic variables - which allow to perform a user-friendly knowledge elicitation. QRAM was, firstly evaluated by “peer” review, with 12 safety experts from Brazil (2), Bulgaria (1), Greece (3), Turkey (3) and Portugal (3), and then, evaluated by comparing QRAM with other RAOS tecnhiques and methods. The safety experts , concluded that: a) QRAM is a versatile tool for occupational safety risk assessment on construction sites; b) the specific checklists for knowledge elicitation are a good decision aid and, c) the use of linguistic variables is a better way to make the risk assessments process more objective and reliable.Fundação para a Ciência e Tecnologia - PhD Scholarship SFRH/BD/39610/200

Integration of the Wang & Mendel algorithm into the application of Fuzzy expert systems to intelligent clinical decision support systems

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.Xunta de Galicia | Ref. ED481A-2020/03