Prediction of stroke probability occurrence based on fuzzy cognitive maps

Among neurological patients, stroke is the most common cause of mortality. It is a health problem that is very costly all over the world. Therefore, the mortality due to the disease can be reduced by identifying and modifying the risk factors. Controllable factors which are contributing to stroke including hypertension, diabetes, heart disease, hyperlipidemia, smoking, and obesity. Therefore, by identifying and controlling the risk factors, stroke can be prevented and the effects of this disease could be reduced to a minimum. Therefore, for the quick and timely diagnosis of the disease, we need an intelligent system to predict the stroke risk. In this paper, a method has been proposed for predicting the risk rate of stroke which is based on fuzzy cognitive maps and nonlinear Hebbian learning algorithm. The accuracy of the proposed NHL-FCM model is tested using 15-fold cross-validation, for 90 actual cases, and compared with those of support vector machine and k-nearest neighbours. The proposed method shows superior performance with a total accuracy of (95.4 ± 7.5)%

Modelo de inteligencia artificial para la detección temprana de diabetes

Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease.Objective. Develop a model based on artificial intelligence to support clinical decision-making in the early detection of diabetes.Materials and Methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity.Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes.Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.Introducción. La diabetes es una enfermedad crónica que se caracteriza por presentar un nivel elevado de glucosa en sangre. Puede generar complicaciones que afectan la calidad de vida y aumentan los costos de la atención en salud. En los últimos años las tasas de prevalencia y mortalidad han aumentado en todo el mundo. El desarrollo de modelos con alto desempeño predictivo puede ayudar en la identificación temprana de la enfermedad.Objetivo. Desarrollar un modelo basado en inteligencia artificial para apoyar la toma de decisiones clínicas en la detección temprana de diabetes.Materiales y Métodos. Realizamos un estudio de corte transversal, utilizando un conjunto de datos que contenía edad, signos y síntomas de pacientes con diabetes e individuos sanos. A los datos se les aplicó técnicas de preprocesamiento. Posteriormente, construimos el modelo basado en mapas cognitivos difusos. El rendimiento fue evaluado con tres métricas: exactitud, especificidad y sensibilidad.Resultados. El modelo desarrollado obtuvo un excelente desempeño predictivo con una exactitud de 95%. Además, permitió identificar el comportamiento de las variables involucradas usando iteraciones simuladas, lo que proporcionó información valiosa sobre la dinámica de los factores de riesgo asociados a la diabetes.Conclusiones. Los mapas cognitivos difusos demostraron un alto valor para la identificación temprana de la enfermedad y en la toma de decisiones clínicas. Los resultados sugieren el potencial de estos enfoques en aplicaciones clínicas relacionadas con la diabetes y respaldan su utilidad en la práctica médica para mejorar los resultados de los pacientes

Predictive and prescriptive modeling for the clinical management of dengue: a case study in Colombia

En esta investigación, abordamos el problema del manejo clínico del dengue, que se compone del diagnóstico y el tratamiento de la enfermedad. El dengue es una enfermedad tropical transmitida por vectores que está ampliamente distribuida en todo el mundo. El desarrollo de enfoques que ayuden a la toma de decisiones en enfermedades de interés para la salud pública –como el dengue– es necesario para reducir las tasas de morbilidad y mortalidad. A pesar de la existencia de guías para el manejo clínico, el diagnóstico y el tratamiento del dengue siguen siendo un reto. Para abordar este problema, nuestro objetivo fue desarrollar metodologías, modelos y enfoques para apoyar la toma de decisiones en relación con el manejo clínico de esta infección. Nosotros desarrollamos varios artículos de investigación para cumplir los objetivos propuestos de esta tesis. El primer articulo revisó las últimas tendencias del modelamiento de dengue usando técnicas de aprendizaje automático. El segundo artículo propuso un sistema de apoyo a la decisión para el diagnóstico del dengue utilizando mapas cognitivos difusos. El tercer artículo propuso un ciclo autónomo de tareas de análisis de datos para apoyar tanto el diagnóstico como el tratamiento de la enfermedad. El cuarto artículo presentó una metodología basada en mapas cognitivos difusos y algoritmos de optimización para generar modelos prescriptivos en entornos clínicos. El quinto artículo puso a prueba la metodología anteriormente mencionada en otros dominios de la ciencia como, por ejemplo, los negocios y la educación. Finalmente, el último artículo propuso tres enfoques de aprendizaje federado para garantizar la seguridad y privacidad de los datos relacionados con el manejo clínico del dengue. En cada artículo evaluamos dichas estrategias utilizando diversos conjuntos de datos con signos, síntomas, pruebas de laboratorio e información relacionada con el tratamiento de la enfermedad. Los resultados mostraron la capacidad de las metodologías y modelos desarrollados para predecir la enfermedad, clasificar a los pacientes según su severidad, evaluar el comportamiento de las variables relacionadas con la severidad y recomendar tratamientos basados en las directrices de la Organización Mundial de la Salud.In this research, we address the problem of clinical management of dengue, which is composed of diagnosis and treatment of the disease. Dengue is a vector-borne tropical disease that is widely distributed worldwide. The development of approaches to aid in decision-making for diseases of public health concern –such as dengue– are necessary to reduce morbidity and mortality rates. Despite the existence of clinical management guidelines, the diagnosis and treatment of dengue remains a challenge. To address this problem, our objective was to develop methodologies, models, and approaches to support decision-making regarding the clinical management of this infection. We developed several research articles to meet the proposed objectives of this thesis. The first article reviewed the latest trends in dengue modeling using machine learning (ML) techniques. The second article proposed a decision support system for the diagnosis of dengue using fuzzy cognitive maps (FCMs). The third article proposed an autonomous cycle of data analysis tasks to support both diagnosis and treatment of the disease. The fourth article presented a methodology based on FCMs and optimization algorithms to generate prescriptive models in clinical settings. The fifth article tested the previously mentioned methodology in other science domains such as, business and education. Finally, the last article proposed three federated learning approaches to guarantee the security and privacy of data related to the clinical management of dengue. In each article, we evaluated such strategies using diverse datasets with signs, symptoms, laboratory tests, and information related to the treatment of the disease. The results showed the ability of the developed methodologies and models to predict disease, classify patients according to severity, evaluate the behavior of severity-related variables, and recommend treatments based on World Health Organization (WHO) guidelines

Decision-Support for Rheumatoid Arthritis Using Bayesian Networks: Diagnosis, Management, and Personalised Care.

PhD Theses.Bayesian networks (BNs) have been widely proposed for medical decision support. One advantage of a BN is reasoning under uncertainty, which is pervasive in medicine. Another advantage is that a BN can be built from both data and knowledge and so can be applied in circumstances where a complete dataset is not available. In this thesis, we examine how BNs can be used for the decision support challenges of chronic diseases. As a case study, we study Rheumatoid Arthritis (RA), which is a chronic inflammatory disease causing swollen and painful joints. The work has been done as part of a collaborative project including clinicians from Barts and the London NHS Trust involved in the treatment of RA. The work covers three stages of decision support, with progressively less available data. The first decision support stage is diagnosis. Various criteria have been proposed by clinicians for early diagnosis but these criteria are deterministic and so do not capture diagnostic uncertainty, which is a concern for patients with mild symptoms in the early stages of the disease. We address this problem by building a BN model for diagnosing RA. The diagnostic BN model is built using both a dataset of 360 patients provided by the clinicians and their knowledge as experts in this domain. The choice of factors to include in the diagnostic model is informed by knowledge, including a model of the care pathway which shows what information is available for diagnosis. Knowledge is used to classify the factors as risk factors, relevant comorbidities, evidence of pathogenesis mechanism, signs, symptoms, and serology results, so that the structure of BN model matches the clinical understanding of RA. Since most of the factors are present in the dataset, we are able to train the parameters of the diagnostic BN from the data. This diagnostic BN model obtains promising results in differentiating RA cases from other inflammatory arthritis cases. Aware that eliciting knowledge is time-consuming and could limit the uptake of these techniques, we consider two alternative approaches. First, we compare its diagnostic performance with an alternative BN model entirely learnt from data; we argue that having a clinically meaningful structure allows us to explain clinical scenarios in a way that cannot be done with the model learnt purely from data. We also examine whether useful knowledge can be retrieved from existing vi medical ontologies, such as SNOMED CT and UMLS. Preliminary results show that it could be feasible to use such sources to partially automate knowledge collection. After patients have been diagnosed with RA, they are monitored regularly by a clinical team until the activity of their disease becomes low. The typical care arrangement has two challenges: first, regular meetings with clinicians occur infrequently at fixed intervals (e.g., every six months), during which time the activity of the disease can increase (or ‘flare’) and decrease several times. Secondly, the best medications or combinations of medications must be found for each patient, but changes can only be made when the patient visits the clinic. We therefore develop this stage of decision support in two parts: the first and simplest part looks at how the frequency of clinic appointments could be varied; the second part builds on this to support decisions to adjust medication dosage. We describe this as the ‘self-management’ decision support model. Disease activity is commonly measured with Disease Activity Score 28 (DAS28). Since the joint count parts of this can be assessed by the patient, the possibility of collecting regular (e.g., weekly) DAS28 data has been proposed. It is not yet in wide use, perhaps because of the overheads to the clinical team of reviewing data regularly. The dataset available to us for this work came from a feasibility study conducted by the clinical collaborators of one system for collecting data from patients, although the frequency is only quarterly. The aim of the ‘self-management’ decision support system is therefore to sit between patient-entered data and the clinical team, saving the work of clinically assessing all the data. Specifically, in the first part we wish to predict disease activity so that an appointment should be made sooner, distinguishing this from patients whose disease is well-managed so that the interval between appointments can be increased. To achieve this, we build a dynamic BN (DBN) model to monitor disease activity and to indicate to patients and their clinicians whether a clinical review is needed. We use the data and a set of dummy patient scenarios designed by the experts to evaluate the performance of the DBN. The second part of the ‘self-management’ decision support stage extends the DBN to give advice on adjustments to the medication dosage. This is of particular clinical interest since one class of medications used (biological disease-modifying antirheumatic drugs) are very expensive and, although effective at reducing disease activity, can have severe adverse reactions. For both these reasons, decision support that allowed a patient to ‘taper’ the dosage of medications without frequent clinic visits would be very useful. This extension does not meet all the decision support needs, which ideally would also cover decision-making about the choice of medications. However, we have found that as yet there is neither sufficient data nor knowledge for this. vii The third and final stage of decision support is targeted at patients who live with RA. RA can have profound impacts on the quality of life (QoL) of those who live with it, affecting work, financial status, friendships, and relationships. Information from patient organisations such as the leaflets prepared by the National Rheumatoid Arthritis Society (NRAS) contains advice on managing QoL, but the advice is generic, leaving it up to each patient to select the advice most relevant to their specific circumstances. Our aim is therefore to build a BN-based decision support system to personalise the recommendations for enhancing the QoL of RA patients. We have built a BN to infer three components of QoL (independence, participation, and empowerment) and shown how this can be used to target advice. Since there is no data, the BN is developed from expert knowledge and literature. To evaluate the resulting system, including the BN, we use a set of patient interviews conducted and coded by our collaborators. The recommendations of the system were compared with those of experts in a set of test scenarios created from the interviews; the comparison shows promising results