289 research outputs found
Development of Machine Learning Techniques for Diabetic Retinopathy Risk Estimation
La retinopatia diabètica (DR) és una malaltia crònica. És una de les principals complicacions de
diabetis i una causa essencial de pèrdua de visió entre les persones que pateixen diabetis.
Els pacients diabètics han de ser analitzats periòdicament per tal de detectar signes de
desenvolupament de la retinopatia en una fase inicial. El cribratge precoç i freqüent disminueix
el risc de pèrdua de visió i minimitza la cà rrega als centres assistencials. El nombre
dels pacients diabètics està en augment i creixements rà pids, de manera que el fa difÃcil
que consumeix recursos per realitzar un cribatge anual a tots ells.
L’objectiu principal d’aquest doctorat. la tesi consisteix en construir un sistema de suport de decisions clÃniques
(CDSS) basat en dades de registre de salut electrònic (EHR). S'utilitzarà aquest CDSS per estimar el risc de desenvolupar RD.
En aquesta tesi doctoral s'estudien mètodes d'aprenentatge automà tic per constuir un CDSS basat en regles lingüÃstiques difuses. El coneixement expressat en aquest tipus de regles facilita que el metge sà piga quines combindacions de les condicions són les poden provocar el risc de desenvolupar RD.
En aquest treball, proposo un mètode per reduir la incertesa en la classificació dels
pacients que utilitzen arbres de decisió difusos (FDT). A continuació es combinen diferents arbres, usant la tècnica de
Fuzzy Random Forest per millorar la qualitat de la predicció.
A continuació es proposen diverses tècniques d'agregació que millorin la fusió dels resultats que ens dóna
cadascun dels arbres FDT. Per millorar la decisió final dels nostres models, proposo tres mesures difuses que
s'utilitzen amb integrals de Choquet i Sugeno. La definició d’aquestes mesures difuses es basa en els valors de confiança de les regles. En particular, una d'elles és una mesura difusa que es troba en la qual
l'estructura jerà rquica de la FDT és explotada per trobar els valors de la mesura difusa.
El resultat final de la recerca feta ha donat lloc a un programari que es pot instal·lar en centres d’assistència primà ria i hospitals, i pot ser usat pels metges de capçalera per fer l'avaluació preventiva i el cribatge de la Retinopatia Diabètica.La retinopatÃa diabética (RD) es una enfermedad crónica. Es una de las principales complicaciones de
diabetes y una causa esencial de pérdida de visión entre las personas que padecen diabetes.
Los pacientes diabéticos deben ser examinados periódicamente para detectar signos de diabetes.
desarrollo de retinopatÃa en una etapa temprana. La detección temprana y frecuente disminuye
el riesgo de pérdida de visión y minimiza la carga en los centros de salud. El número
de pacientes diabéticos es enorme y está aumentando rápidamente, lo que lo hace difÃcil y
Consume recursos para realizar una evaluación anual para todos ellos.
El objetivo principal de esta tesis es construir un sistema de apoyo a la decisión clÃnica
(CDSS) basado en datos de registros de salud electrónicos (EHR). Este CDSS será utilizado
para estimar el riesgo de desarrollar RD.
En este tesis doctoral se estudian métodos de aprendizaje automático para construir un CDSS basado
en reglas lingüÃsticas difusas. El conocimiento expresado en este tipo de reglas facilita que el médico
pueda saber que combinaciones de las condiciones son las que pueden provocar el riesgo de desarrollar RD.
En este trabajo propongo un método para reducir la incertidumbre en la clasificación de los
pacientes que usan árboles de decisión difusos (FDT). A continuación se combinan diferentes árboles usando
la técnica de Fuzzy Random Forest para mejorar la calidad de la predicción.
Se proponen también varias polÃticas para fusionar los resultados de que nos da cada uno de los árboles (FDT).
Para mejorar la decisión final propongo tres medidas difusas que se usan con las integrales Choquet y Sugeno.
La definición de estas medidas difusas se basa en los valores de confianza de
las reglas. En particular, uno de ellos es una medida difusa descomponible en la que se usa
la estructura jerárquica del FDT para encontrar los valores de la medida difusa.
Como resultado final de la investigación se ha construido un software que puede instalarse en centros de atención médica y hospitales, i que puede ser usado por los médicos de cabecera para hacer la evaluación preventiva y
el cribado de la RetinopatÃa Diabética.Diabetic retinopathy (DR) is a chronic illness. It is one of the main complications of
diabetes, and an essential cause of vision loss among people suffering from diabetes.
Diabetic patients must be periodically screened in order to detect signs of diabetic
retinopathy development in an early stage. Early and frequent screening decreases
the risk of vision loss and minimizes the load on the health care centres. The number
of the diabetic patients is huge and rapidly increasing so that makes it hard and
resource-consuming to perform a yearly screening to all of them.
The main goal of this Ph.D. thesis is to build a clinical decision support system
(CDSS) based on electronic health record (EHR) data. This CDSS will be utilised
to estimate the risk of developing RD.
In this Ph.D. thesis, I focus on developing novel interpretable machine learning
systems. Fuzzy based systems with linguistic terms are going to be proposed. The
output of such systems makes the physician know what combinations of the features
that can cause the risk of developing DR.
In this work, I propose a method to reduce the uncertainty in classifying diabetic
patients using fuzzy decision trees. A Fuzzy Random forest (FRF) approach is
proposed as well to estimate the risk for developing DR.
Several policies are going to be proposed to merge the classification results
achieved by different Fuzzy Decision Trees (FDT) models to improve the quality of
the final decision of our models, I propose three fuzzy measures that are used with Choquet and Sugeno integrals.
The definition of these fuzzy measures is based on the confidence values of
the rules. In particular, one of them is a decomposable fuzzy measure in which the
hierarchical structure of the FDT is exploited to find the values of the fuzzy measure.
Out of this Ph.D. work, we have built a CDSS software that may be installed in the health care centres and hospitals
in order to evaluate and detect Diabetic Retinopathy at early stages
Learning fuzzy measures for aggregation in fuzzy rule-based models
Comunicación presentada al 15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 (15 - 18 october 2018).Fuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno $$\lambda $$ -measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature.This work is supported by the URV grant 2017PFR-URV-B2-60, and by the Spanish research projects no: PI12/01535 and PI15/01150 for (Instituto de Salud Carlos III and FEDER funds). Mr. Saleh has a Pre-doctoral grant (FI 2017) provided by the Catalan government and an Erasmus+ travel grant by URV. Prof. Bustince acknowledges the support of Spanish project TIN2016-77356-P
RFFE – Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus
Diabetes is a category of metabolic disease commonly known as a chronic illness. It causes the body to generate less insulin and raises blood sugar levels, leading to various issues and disrupting the functioning of organs, including the retinal, kidney and nerves. To prevent this, people with chronic illnesses require lifetime access to treatment. As a result, early diabetes detection is essential and might save many lives. Diagnosis of people at high risk of developing diabetes is utilized for preventing the disease in various aspects. This article presents a chronic illness prediction prototype based on a person's risk feature data to provide an early prediction for diabetes with Fuzzy Entropy random vectors that regulate the development of each tree in the Random Forest. The proposed prototype consists of data imputation, data sampling, feature selection, and various techniques to predict the disease, such as Fuzzy Entropy, Synthetic Minority Oversampling Technique (SMOTE), Convolutional Neural Network (CNN) with Stochastic Gradient Descent with Momentum (SGDM), Support Vector Machines (SVM), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). This study uses the existing Pima Indian Diabetes (PID) dataset for diabetic disease prediction. The predictions' true/false positive/negative rate is investigated using the confusion matrix and the receiver operating characteristic area under the curve (ROCAUC). Findings on a PID dataset are compared with machine learning algorithms revealing that the proposed Random Forest Fuzzy Entropy (RFFE) is a valuable approach for diabetes prediction, with an accuracy of 98 percent
Computational Intelligence in Healthcare
This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic
Multi-modal Predictive Models of Diabetes Progression
With the increasing availability of wearable devices, continuous monitoring
of individuals' physiological and behavioral patterns has become significantly
more accessible. Access to these continuous patterns about individuals'
statuses offers an unprecedented opportunity for studying complex diseases and
health conditions such as type 2 diabetes (T2D). T2D is a widely common chronic
disease that its roots and progression patterns are not fully understood.
Predicting the progression of T2D can inform timely and more effective
interventions to prevent or manage the disease. In this study, we have used a
dataset related to 63 patients with T2D that includes the data from two
different types of wearable devices worn by the patients: continuous glucose
monitoring (CGM) devices and activity trackers (ActiGraphs). Using this
dataset, we created a model for predicting the levels of four major biomarkers
related to T2D after a one-year period. We developed a wide and deep neural
network and used the data from the demographic information, lab tests, and
wearable sensors to create the model. The deep part of our method was developed
based on the long short-term memory (LSTM) structure to process the time-series
dataset collected by the wearables. In predicting the patterns of the four
biomarkers, we have obtained a root mean square error of 1.67% for HBA1c, 6.22
mg/dl for HDL cholesterol, 10.46 mg/dl for LDL cholesterol, and 18.38 mg/dl for
Triglyceride. Compared to existing models for studying T2D, our model offers a
more comprehensive tool for combining a large variety of factors that
contribute to the disease
Appositeness of artificial intelligence in modern medicine
Artificial intelligence (AI) can be demonstrated as intelligence demonstrated by machines.AI research has gone through different phases like simulating the brain, modeling human problem solving, formal logic, large databases of knowledge and imitating animal behavior. In the beginning of 21st century, highly mathematical statistical machine learning has dominated the field, was found useful and considered in helping to solve many challenging problems throughout industry and academia. The domain was discovered and work was done on the assumption that human intelligence can be simulated by machines. These initiate some discussions in raising queries about the mind and the ethics of creating artificial beings with human-like intelligence. Myth, fiction, and philosophy are involved in the creation of this field. The debates and discussion also point to concerns of misuse regarding this technology.
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Artificial intelligence (AI) models are increasingly finding applications in
the field of medicine. Concerns have been raised about the explainability of
the decisions that are made by these AI models. In this article, we give a
systematic analysis of explainable artificial intelligence (XAI), with a
primary focus on models that are currently being used in the field of
healthcare. The literature search is conducted following the preferred
reporting items for systematic reviews and meta-analyses (PRISMA) standards for
relevant work published from 1 January 2012 to 02 February 2022. The review
analyzes the prevailing trends in XAI and lays out the major directions in
which research is headed. We investigate the why, how, and when of the uses of
these XAI models and their implications. We present a comprehensive examination
of XAI methodologies as well as an explanation of how a trustworthy AI can be
derived from describing AI models for healthcare fields. The discussion of this
work will contribute to the formalization of the XAI field.Comment: 15 pages, 3 figures, accepted for publication in the IEEE
Transactions on Artificial Intelligenc
Computational Intelligence in Healthcare
The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications
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