Christos Bellos

Identification of COPD Patients’ Health Status Using an Intelligent System in the CHRONIOUS Wearable Platform

The CHRONIOUS system offers an integrated platform aiming at the effective management and real-time assessment of the health status of the patient suffering from chronic obstructive pulmonary disease (COPD). An intelligent system is developed for the analysis and the real-time evaluation of patients condition. A hybrid classifier has been implemented on a personal digital assistant, combining a support vector machine, a random forest, and a rule-based system to provide a more advanced categorization scheme for the early and in real-time characterization of a COPD episode. This is followed by a severity estimation algorithm which classifies the identified pathological situation in different levels and triggers an alerting mechanism to provide an informative and instructive message/advice to the patient and the clinical supervisor. The system has been validated using data collected from 30 patients that have been annotated by experts indicating 1) the severity level of the current patients health status, and 2) the COPD disease level of the recruited patients according to the GOLD guidelines. The achieved characterization accuracy has been found 94%.

An intelligent system for classification of patients suffering from chronic diseases

The CHRONIOUS system addresses a smart wearable platform, based on multi-parametric sensor data processing, for monitoring people suffering from chronic diseases in long-stay setting. Several signals are being recorded through wearable sensors and are stored together with additional information, entered by the patient. An Intelligent System, placed at a Smart Assistant Device, analyzes incoming data and facilitates data mining techniques resulting upon the severity of a health episode. Part of the Intelligent System is the Mental Support Tool, which calculates a Stress Index and classifies the mental condition and stress levels of the patient. An additional component aiming at the personalization of the Intelligent Systems Decision is the Profiler which defines several patients profiles and facilitates clustering techniques in order to associate each patients description with one of the predefined profiles.

CHRONIOUS: A wearable platform for monitoring and management of patients with chronic disease

The CHRONIOUS system has been developed based on an open architecture design that consists of a set of subsystems which interact in order to provide all the needed services to the chronic disease patients. An advanced multi-parametric expert system is being implemented that fuses information effectively from various sources using intelligent techniques. Data are collected by sensors of a body network controlling vital signals while additional tools record dietary habits and plans, drug intake, environmental and biochemical parameters and activity data. The CHRONIOUS platform provides guidelines and standards for the future generations of “chronic disease management systems” and facilitates sophisticated monitoring tools. In addition, an ontological information retrieval system is being delivered satisfying the necessities for up-to-date clinical information of Chronic Obstructive pulmonary disease (COPD) and Chronic Kidney Disease (CKD). Moreover, support tools are being embedded in the system, such as the Mental Tools for the monitoring of patient mental health status. The integrated platform provides real-time patient monitoring and supervision, both indoors and outdoors and represents a generic platform for the management of various chronic diseases.

Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management

CHRONIOUS system is an integrated platform aiming at the management of chronic disease patients. One of the most important components of the system is a Decision Support System (DSS) that has been developed in a Smart Device (SD). This component decides on patients current health status by combining several data, which are acquired either by wearable sensors or manually inputted by the patient or retrieved from the specific database. In case no abnormal situation has been tracked, the DSS takes no action and remains deactivated until next abnormal situation pack of data are being acquired or next scheduled data being transmitted. The DSS that has been implemented is an integrated classification system with two parallel classifiers, combining an expert system (rule-based system) and a supervised classifier, such as Support Vector Machines (SVM), Random Forests, artificial Neural Networks (aNN like the Multi-Layer Perceptron), Decision Trees and Naïve Bayes. The above categorized system is useful for providing critical information about the health status of the patient.

Towards a semantic representation for multi-scale finite element biosimulation experiments

Biosimulation researchers use a variety of models, tools and languages for capturing and processing different aspects of biological processes. However, current modeling methods do not capture the underlying semantics of the biosimulation models sufficiently to support building, reusing, composing and merging complex biosimulation models originating from diverse experiments. In this paper, we propose an ontology based and multi-layered biosimulation model to facilitate researchers to share, integrate and collaborate their knowledge bases at Web scale. In particular, we investigate the semantic biosimulation model under the context of the multi-scale finite element (FE) modelling of the inner-ear. The proposed ontology-based biosimulation model will provide a homogenized and standardized access to the shared, semantically integrated and harmonized datasets for clinical data (histological data, micro-CT images of the cochlea, pathological data) and inner ear FE simulation models. The work presented in this paper is analyzed and designed as part of the SIFEM EU project.

A Support Vector Machine Approach for Categorization of Patients Suffering from Chronic Diseases

The CHRONIOUS system is an open-architecture integrated platform aiming at the management of chronic disease patients. The system consists of a body sensor network collecting patient’s vital signals, a Personal Digital Assistance (PDA) for the real-time data analysis based on a Decision Support System (DSS) and a central system for the deeper analysis of patient’s status and data storing. The DSS combines several data sources to decide upon the severity of patient’s current health status. The first pilot study has been designed and carried out using patients suffering from Chronic Obstructive Pulmonary Disease(COPD). The DSS facilitates a one-against-all multi-class Support Vector Machine (SVM) classification system. The performance of the categorization scheme provides high classification results for most of the patient’s health status levels. The involvement of a larger number of patients might increase further the performance of the system.

Clinical validation of the CHRONIOUS wearable system in patients with chronic disease

The CHRONIOUS system defines a powerful and easy to use framework which has been designed to provide services to clinicians and their patients suffering from chronic diseases. The system is composed of a wearable shirt that integrate several body sensors, a portable smart device and a central sub-system that is responsible for the long term storage of the collected patients data. A multi-parametric expert system is developed for the analysis of the collected data using intelligent algorithms and complex techniques. Apart for the vital signals, dietary habits, drug intake, activity data, environmental and biochemical parameters are recorded. The CHRONIOUS platform is validated through clinical trials in several medical centers and patients home environments recruiting patients suffering from Chronic Obstructive pulmonary disease (COPD) and Chronic Kidney Disease (CKD) diseases. The clinical trials contribute in improving the systems accuracy, while Pulmonologists and Nephrologists experts utilized the CHRONIOUS platform to evaluate its efficiency and performance. The results of the utilization of the system were very encouraging. The CHRONIOUS system has been proven to be a well-validated real-time patient monitoring and supervision platform, providing a useful tool for the clinician and the patient that would contribute to the more effective management of chronic diseases.

SIFEM project: Semantic infostructure interlinking an open source finite element tool and libraries with a model repository for the multi-scale modelling of the inner-ear

The SIFEM project targets the development of an infrastructure in order to semantically link open source tools and libraries with existing data as well as new knowledge towards the multi-scale finite element modelling of the inner-ear. The SIFEM system is designed based on an open architecture schema that consists of a set of tools and subsystems in order to develop robust multi-scale models. The project mainly delivers: (i) tools for finite elements modelling, (ii) cochlea reconstruction tool and (iii) 3D inner ear models visualization tool. The main scientific results contribute to the knowledge of alterations associated to diverse cochlear disorders and could lead, in long-term, to personalized healthcare. The overview of the SIFEM platform and its architecture is presented in this paper.

Biologically inspired near extinct system reconstruction

Recovery software system operations from a state of extensive damage without human intervention is a challenging problem as it may need to be based on a different infrastructure from the one that the system was originally designed for and deployed on (i.e., computational and communication devices) and significant reorganization of system functionalities. In this paper, we introduce a bio-inspired approach for reconstructing nearly extinct complex software systems. Our approach is based on encoding a computational DNA (co-DNA) of a system and computational analogues of biological processes to enable the transmission of co-DNA over computational devices and, through it, the transformation of these devices into system cells that can realise chunks of the system functionality, and spread further its reconstruction process.

Categorization of COPD patient's health level through the use of the CHRONIOUS wearable platform

The Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease that causes airflow blockage and breathing-related problems. As a Chronic disease it requires specific treatment plan and patient management for a long period of time. Critical factor in the process is the realization of frequent and precise diagnostic tests that describes the health status of the patient. The CHRONIOUS system provides the required easy-to-use wearable platform aiming at the successful management of COPD patients. Several signals and patients data are stored by the utilization of an ergonomic jacket and through the patients platform interface. Hybrid techniques based on supervised and unsupervised methodologies were applied for the analysis of the patients situation. The categorization of health level of the patient to discrete levels is achieved in a continuous base. Useful outcomes in the form of message or advice are extracted appeared on patients and clinicians devices denoting his health status.