Recent Developments in Computer Methods for fMRI Data Processing

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A six stage approach for the diagnosis of the Alzheimer’s disease based on fMRI data

AbstractThe aim of this work is to present an automated method that assists in the diagnosis of Alzheimer’s disease and also supports the monitoring of the progression of the disease. The method is based on features extracted from the data acquired during an fMRI experiment. It consists of six stages: (a) preprocessing of fMRI data, (b) modeling of fMRI voxel time series using a Generalized Linear Model, (c) feature extraction from the fMRI data, (d) feature selection, (e) classification using classical and improved variations of the Random Forests algorithm and Support Vector Machines, and (f) conversion of the trees, of the Random Forest, to rules which have physical meaning. The method is evaluated using a dataset of 41 subjects. The results of the proposed method indicate the validity of the method in the diagnosis (accuracy 94%) and monitoring of the Alzheimer’s disease (accuracy 97% and 99%)

KardiaTool: An Integrated POC Solution for Non-invasive Diagnosis and Therapy Monitoring of Heart Failure Patients

The aim of this work is to present KardiaTool platform, an integrated Point of Care (POC) solution for noninvasive diagnosis and therapy monitoring of Heart Failure (HF) patients. The KardiaTool platform consists of two components, KardiaPOC and KardiaSoft. KardiaPOC is an easy to use portable device with a disposable Lab-on-Chip (LOC) for the rapid, accurate, non-invasive and simultaneous quantitative assessment of four HF related biomarkers, from saliva samples. KardiaSoft is a decision support software based on predictive modeling techniques that analyzes the POC data and other patient's data, and delivers information related to HF diagnosis and therapy monitoring. It is expected that identifying a source comparable to blood, for biomarker information extraction, such as saliva, that is cost-effective, less invasive, more convenient and acceptable for both patients and healthcare professionals would be beneficial for the healthcare community. In this work the architecture and the functionalities of the KardiaTool platform are presented

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented. Keywords: Heart failure, Diagnosis, Prediction, Severity estimation, Classification, Data minin

Crafting vascular medicine training scenarios: The RT3S authoring tool

Summarization: The RT3S E-learning environment enables experts in vascular medicine to prepare educational training content for their trainees (i.e., future endovascular surgeons). Influenced by the Learning Design (LD) information model and building-upon LAMS, the learning process is realized by means of training scenarios (i.e., as an interactive sequence of learning steps). In RT3S, creating learning scenarios does not require that the editors of the scenarios are familiar with the underlying LAMS environment. The RT3S authoring environment is easy to use and customized (i.e., it can be adapted to the needs of the tutor and of the scenario) and enables tutors (e.g., expert surgeons) to easily prepare new educational content. Students (future surgeons) are trained on the assessment of real (and realistic) patient data and on decision-making processes for the management and treatment of patients.Παρουσιάστηκε στο: IEEE 13th International Conference on Bioinformatics and Bioengineerin

E-learning templates for peripheral vascular stenting

Summarization: The RT3S training application is designed to enable tutors (expert surgeons) prepare educational content for their students, and the students (future surgeons) to be trained on the assessment of real patient cases on peripheral vascular stenting. It incorporates state-of-art technologies (i.e., LAMS) for system design and adopts the latest e-learning standards (i.e., IMS Learning Design) for the implementation of training scenarios. In this work, we focus on the structure of the clinical scenarios that constitute the core of the RT3S training application. In RT3S, creating or accessing learning scenarios does not require that the author (tutor) or the learner (student) be familiar with any programming environment. The learning scenario allows the learners to follow an educational scenario and interact with their tutor.Παρουσιάστηκε στο: 15th International Conference on E-Health Networking Applications and Service

Real-time simulation for safer vascular stenting-the training application

Summarization: We present a novel web-design e-learning platform for vascular surgeons of peripheral arteries. The training application enables experts to prepare educational, training content for their trainees (medical students, endovascular surgeons at their first steps in the specific competence) and the trainees to use real patient data, process the data, go through the whole condition assessment and, finally, take all the important decisions suggested by a training scenario.Presented on

A Machine Learning Approach for Chronic Heart Failure Diagnosis

The aim of this study was to address chronic heart failure (HF) diagnosis with the application of machine learning (ML) approaches. In the present study, we simulated the procedure that is followed in clinical practice, as the models we built are based on various combinations of feature categories, e.g., clinical features, echocardiogram, and laboratory findings. We also investigated the incremental value of each feature type. The total number of subjects utilized was 422. An ML approach is proposed, comprising of feature selection, handling class imbalance, and classification steps. The results for HF diagnosis were quite satisfactory with a high accuracy (91.23%), sensitivity (93.83%), and specificity (89.62%) when features from all categories were utilized. The results remained quite high, even in cases where single feature types were employed

A computational approach for the estimation of heart failure patients status using saliva biomarkers

The aim of this work is to present a computational approach for the estimation of the severity of heart failure (HF) in terms of New York Heart Association (NYHA) class and the characterization of the status of the HF patients, during hospitalization, as acute, progressive or stable. The proposed method employs feature selection and classification techniques. However, it is differentiated from the methods reported in the literature since it exploits information that biomarkers fetch. The method is evaluated on a dataset of 29 patients, through a 10-fold-cross-validation approach. The accuracy is 94 and 77% for the estimation of HF severity and the status of HF patients during hospitalization, respectively