Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases

Acute lung injury in paediatric intensive care: course and outcome

Introduction: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) carry a high morbidity and mortality (10-90%). ALI is characterised by non-cardiogenic pulmonary oedema and refractory hypoxaemia of multifactorial aetiology [1]. There is limited data about outcome particularly in children. Methods This retrospective cohort study of 85 randomly selected patients with respiratory failure recruited from a prospectively collected database represents 7.1% of 1187 admissions. They include those treated with High Frequency Oscillation Ventilation (HFOV). The patients were admitted between 1 November 1998 and 31 October 2000. Results: Of the 85, 49 developed acute lung injury and 47 had ARDS. There were 26 males and 23 females with a median age and weight of 7.7 months (range 1 day-12.8 years) and 8 kg (range 0.8-40 kg). There were 7 deaths giving a crude mortality of 14.3%, all of which fulfilled the Consensus I [1] criteria for ARDS. Pulmonary occlusion pressures were not routinely measured. The A-a gradient and PaO2/FiO2 ratio (median + [95% CI]) were 37.46 [31.82-43.1] kPa and 19.12 [15.26-22.98] kPa respectively. The non-survivors had a significantly lower PaO2/FiO2 ratio (13 [6.07-19.93] kPa) compared to survivors (23.85 [19.57-28.13] kPa) (P = 0.03) and had a higher A-a gradient (51.05 [35.68-66.42] kPa) compared to survivors (36.07 [30.2-41.94]) kPa though not significant (P = 0.06). Twenty-nine patients (59.2%) were oscillated (Sensormedics 3100A) including all 7 non-survivors. There was no difference in ventilation requirements for CMV prior to oscillation. Seventeen of the 49 (34.7%) were treated with Nitric Oxide including 5 out of 7 non-survivors (71.4%). The median (95% CI) number of failed organs was 3 (1.96-4.04) for non-survivors compared to 1 (0.62-1.62) for survivors (P = 0.03). There were 27 patients with isolated respiratory failure all of whom survived. Six (85.7%) of the non-survivors also required cardiovascular support.Conclusion: A crude mortality of 14.3% compares favourably to published data. The A-a gradient and PaO2/FiO2 ratio may be of help in morbidity scoring in paediatric ARDS. Use of Nitric Oxide and HFOV is associated with increased mortality, which probably relates to the severity of disease. Multiple organ failure particularly respiratory and cardiac disease is associated with increased mortality. ARDS with isolated respiratory failure carries a good prognosis in children

Platform for AI-driven medical data analysis to support clinical decision

Cancer is one of the leading causes of death on the world and surviving its treatment does not mean that the process is over. Several patients that have undergone cancer treatment, feel insecure in relation to their health, due to the stress and anxiety of cancer reappearance and post-treatment symptoms such as: sleeping disorders, fatigue and memory problems, pain, anxiety, and stress. Patients that undergone cancer treatment are followed periodically by a clinician, that evaluates its clinical situation, but also, his Quality of Life. This information is vital to understand the patient well-being, since cancer as a huge impact on all aspects of the patient’s life. Nevertheless, clinicians lack on tools capable of measuring objectively the patient’s Quality of Life, nor tools that enable more data visualization that could improve the clinician’s decision-making. So, the purposed aim of this dissertation is to provide a Clinical Decision Support System Platform with visualization tools capable of giving information from patients, gathered from a wearable device and a smart scale, and using Fuzzy Logic, an Artificial Intelligence subset, to give new insights about patient well-being. The designed CDSS Platform was able to integrate commercially used smart device, with minimal human intervention required. Also, the data gathered from those devices was used to create a continuous monitoring system, associated with visualization tools that enhanced the clinician knowledge of the patient. Furthermore, an indicator denominated as Patient Progression Indicator was developed with the use of the Fuzzy Logic algorithm, that provides an indirect but objective measurement of the patient well-being. Although the results seem promising, more in-depth research is required such as a trial study capable of validating the results obtained.O cancro é umas das maiores causas de morte no mundo e sobreviver ao seu tratamento não significa que o processo tenha terminado. Vários pacientes que ultrapassaram o processo de tratamento permanecem inseguros em relação à sua saúde, devido ao stress e ansiedade causados pelo medo de reaparecimento do cancro e pelos efeitos do tratamento tais como: problemas de sono, cansaço e problemas de memória, dor, ansiedade e stress. Os pacientes que terminam o tratamento são seguidos periodicamente por clínicos, que avaliam a sua Qualidade de Vida. Esta informação é essencial para compreender o seu estado de saúde, dado que o cancro tem um impacto enorme em todos os aspetos da vida do paciente. No entanto, os clínicos têm à sua disposição poucas ferramentas capazes de mensurar objetivamente a Qualidade de Vida, ou de ferramentas que possibilitem uma maior visualização de dados que proporcione uma melhor tomada de decisão. Portanto, a solução proposta nesta dissertação é a de desenvolver um Sistema de Apoio à Decisão Clínica com ferramentas de visualização capazes de disponibilizar mais informação do paciente, obtidas com o uso de uma pulseira inteligente e uma balança inteligente. Também com o uso de Lógica Difusa, um subconjunto da Inteligência Artificial, proporcionar uma nova informação sobre o estado de saúde do paciente. A plataforma projetada foi capaz de integrar dispositivos inteligentes de uso comercial, de forma a necessitar o mínimo de interação humana. Além disso, os dados adquiridos pelos dispositivos foram usados para criar um sistema de monitorização contínuo, associado a ferramentas de visualização de dados que proporcionam mais informação em relação ao paciente. Mais ainda, foi desenvolvido um indicador designado por Indicador de Progresso do Paciente com a utilização do algoritmo de Lógica Difusa, que providência uma forma indireta, mas objetiva de mensurar o estado de saúde do paciente. Apesar dos resultados parecerem promissores, um estudo mais aprofundado é necessário, tal como um ensaio clínico capaz de validar os resultados obtidos

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Multimorbidity burden and dementia risk in older adults : The role of inflammation and genetics

Funding: Swedish National study on Aging and Care; Ministry of Health and Social Affairs; Swedish Research Council, Grant/Award Number: 2016-00981; Swedish Research Council for Health,Working Life andWelfare, Grant/Award Number: 2017-01764; Italian Ministry of Health, Grant/Award Number: PE-2016-02364885We investigate dementia risk in older adults with different disease patterns and explore the role of inflammation and apolipoprotein E (APOE) genotype. A total of 2,478 dementia-free participants with two or more chronic diseases (ie, multimorbidity) part of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) were grouped according to their multimorbidity patterns and followed to detect clinical dementia. The potential modifier effect of C-reactive protein (CRP) and apolipoprotein E (APOE) genotype was tested through stratified analyses. People with neuropsychiatric, cardiovascular, and sensory impairment/cancer multimorbidity had increased hazards for dementia compared to the unspecific (Hazard ration (HR) 1.66, 95% confidence interval [CI] 1.13-2.42; 1.61, 95% CI 1.17-2.29; 1.32, 95% CI 1.10-1.71, respectively). Despite the lack of statistically significant interaction, high CRP increased dementia risk within these patterns, and being APOE ε4 carriers heightened dementia risk for neuropsychiatric and cardiovascular multimorbidity. Individuals with neuropsychiatric, cardiovascular, and sensory impairment/cancer patterns are at increased risk for dementia and APOE ε4, and inflammation may further increase the risk. Identifying such high-risk groups might allow tailored interventions for dementia prevention

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models