Ioanna Chouvarda

Home care delivery through the mobile telecommunications platform: The Citizen Health System (CHS) perspective

Health delivery practices are shifting towards home care. The reasons are the better possibilities for managing chronic care, controlling health delivery costs, increasing quality of life and quality of health services and the distinct possibility of predicting and thus avoiding serious complications. For the above goals to become routine, new telemedicine and information technology (IT) solutions need to be implemented and integrated in the health delivery scene, and these solutions need to be assessed through evidence-based medicine in order to provide solid proof for their usefulness. Thus, the concept of contact or call centers has emerged as a new and viable reality in the field of IT for health and telemedicine. In this paper we describe a generic contact center that was designed in the context of an EU funded IST for health project with acronym Citizen Health System (CHS). Since the generic contact center is composed by a number of modules, we shall concentrate in the modules dealing with the communication between the patient and the contact center using mobile telecommunications solutions, which can act as link between the internet and the classical computer telephony communication means. We further elaborate on the development tools of such solutions, the interface problems we face, and on the means to convey information from and to the patient in an efficient and medically acceptable way. This application proves the usefulness of wireless technology in providing health care services all around the clock and everywhere the citizen is located, it proves the necessity for restructuring the medical knowledge for education delivery to the patient, and it shows the virtue of interactivity by means of using the limited, yet useful browsing capabilities of the wireless application protocol (WAP) technology.

A multiagent system enhancing home-care health services for chronic disease management

In this paper, a multiagent system (MAS) is presented, aiming to enhance monitoring, surveillance, and educational services of a generic medical contact center (MCC) for chronic disease management. In such a home-care scenario, a persistent need arises for efficiently monitoring the patient contacts and the MCCs functionality, in order to effectively manage and interpret the large volume of medical data collected during the patient sessions with the system, and to assess the use of MCC resources. Software agents were adopted to provide the means to accomplish such real-time information-processing tasks, due to their autonomous, reactive and/or proactive nature, and their effectiveness in dynamic environments by incorporating coordination strategies. Specifically, the objective of the MAS is to monitor the MCC environment, detect important cases, and inform the healthcare and administrative personnel via alert messages, notifications, recommendations, and reports, prompting them for actions. The main aim of this paper is to present the overall design and implementation of a proposed MAS, emphasizing its functional model and architecture, as well as on the agent interactions and the knowledge-sharing mechanism incorporated, in the context of a generic MCC.

The citizen health system (CHS): a Modular medical contact center providing quality telemedicine services

In the context of the Citizen Health System (CHS) project, a modular Medical Contact Center (MCC) was developed, which can be used in the monitoring, treatment, and management of chronically ill patients at home, such as diabetic or congestive heart failure patients. The virtue of the CHS contact center is that, using any type of communication and telematics technology, it is able to provide timely and preventive prompting to the patients, thus, achieving better disease management. In this paper, we present the structure of the CHS system, describing the modules that enable its flexible and extensible architecture. It is shown, through specific examples, how quality of healthcare delivery can be increased by using such a system.

Indicators of sleepiness in an ambulatory EEG study of night driving.

Driver sleepiness due to sleep deprivation is a causative factor in 1% to 3% of all motor vehicle crashes. In recent studies, the importance of developing driver fatigue countermeasure devices has been stressed, in order to help prevent driving accidents and errors. Although numerous physiological indicators are available to describe an individuals level of alertness, the EEG signal has been shown to be one of the most predictive and reliable, since it is a direct measure of brain activity. In the present study, multichannel EEG data that were collected from 20 sleep-deprived subjects during real environmental conditions of driving are presented for the first time. EEG datas annotation made by two independent Medical Doctors revealed an increase of slowing activity and an acute increase of the alpha waves 5-10 seconds before driving events. From the EEG data that were collected, the Relative Band Ratio (RBR) of the EEG frequency bands, the Shannon Entropy, and the Kullback-Leibler (KL) Entropy were estimated for each one second segment. The mean values of these measurements were estimated for 5 minutes periods. Analysis revealed a significant increase of alpha waves relevant band ratios (RBR), a decrease of gamma waves RBR, and a significant decrease of KL entropy when the first and the last 5-min periods were compared. A rapid decrease of both Shannon and K-L entropies was observed just before the driving events. Conclusively, EEG can assess effectively the brain activity alterations that occur a few seconds before sleeping/drowsiness events in driving, and its quantitative measurements can be used as potential sleepiness indicators for future development of driver fatigue countermeasure devices

A Personalized Framework for Medication Treatment Management in Chronic Care

The ongoing efforts toward continuity of care and the recent advances in information and communication technologies have led to a number of successful personal health systems for the management of chronic care. These systems are mostly focused on monitoring efficiently the patients medical status at home. This paper aims at extending home care services delivery by introducing a novel framework for monitoring the patients condition and safety with respect to the medication treatment administered. For this purpose, considering a body area network (BAN) with advanced sensors and a mobile base unit as the central communication hub from the one side, and the clinical environment from the other side, an architecture was developed, offering monitoring patterns definition for the detection of possible adverse drug events and the assessment of medication response, supported by mechanisms enabling bidirectional communication between the BAN and the clinical site. Particular emphasis was given on communication and information flow aspects that have been addressed by defining/adopting appropriate formal information structures as well as the service-oriented architecture paradigm. The proposed framework is illustrated via an application scenario concerning hypertension management.

A Pervasive Health System Integrating Patient Monitoring, Status Logging, and Social Sharing

In this paper, we present the design and development of a pervasive health system enabling self-management of chronic patients during their everyday activities. The proposed system integrates patient health monitoring, status logging for capturing various problems or symptoms met, and social sharing of the recorded information within the patients community, aiming to facilitate disease management. A prototype is implemented on a mobile device illustrating the feasibility and applicability of the presented work by adopting unobtrusive vital signs monitoring through a wearable multisensing device, a service-oriented architecture for handling communication issues, and popular microblogging services. Furthermore, a study has been conducted with 16 hypertensive patients, in order to investigate the user acceptance, the usefulness, and the virtue of the proposed system. The results show that the system is welcome by the chronic patients who are especially willing to share healthcare information, and is easy to learn and use, while its features have been overall regarded by the patients as helpful for their disease management and treatment.

Machine Learning and Data Mining Methods in Diabetes Research

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Changes of heart and respiratory rate dynamics during weaning from mechanical ventilation: A study of physiologic complexity in surgical critically ill patients

PURPOSE The aim of the study was to investigate heart rate (HR) and respiratory rate (RR) complexity in patients with weaning failure or success, using both linear and nonlinear techniques. MATERIALS AND METHODS Forty-two surgical patients were enrolled in the study. There were 24 who passed and 18 who failed a weaning trial. Signals were analyzed for 10 minutes during 2 phases: (1) pressure support (PS) ventilation (15-20 cm H(2)O) and (2) weaning trials with PS (5 cm H(2)O). Low- and high-frequency (LF, HF) components of HR signals, HR multiscale entropy (MSE), RR sample entropy, cross-sample entropy between cardiorespiratory signals, Poincaré plots, and α1 exponent were computed in all patients and during the 2 phases of PS. RESULTS Weaning failure patients exhibited significantly decreased RR sample entropy, LF, HF, and α1 exponent, compared with weaning success subjects (P < .001). Their changes were opposite between the 2 phases, except for MSE that increased between and within groups (P < .001). A new model including rapid shallow breathing index (RSBI), α1 exponent, RR, and cross-sample entropies predicted better weaning outcome compared with RSBI, airway occlusion pressure at 0.1 second (P(0.1)), and RSBI × P(0.1) (conventional model, R(2) = 0.887 vs 0.463; P < .001). Areas under the curve were 0.92 vs 0.86, respectively (P < .005). CONCLUSIONS We suggest that nonlinear analysis of cardiorespiratory dynamics has increased prognostic impact upon weaning outcome in surgical patients.

Assessment of the EEG complexity during activations from sleep

The present study quantitatively analyzes the EEG characteristics during activations (Act) that occur during NREM sleep, and constitute elements of sleep microstructure (i.e. the Cyclic Alternating Pattern). The fractal dimension (FD) and the sample entropy (SampEn) measures were used to study the different sleep stages and the Act that build up the sleep structure. Polysomnographic recordings from 10 good sleepers were analyzed. The complexity indexes of the Act were compared with the non-activation (NAct) periods during non-REM sleep. In addition, complexity measures among the different Act subtypes (A1, A2 and A3) were analyzed. A3 presented a quite similar complexity independently of the sleep stage, while A1 and A2 showed higher complexity in light sleep than during deep sleep. The current results suggest that Act present a hierarchic complexity between subtypes A3 (higher), A2 (intermediate) and A1 (lower) in all sleep stages.

An Open and Reconfigurable Wireless Sensor Network for Pervasive Health Monitoring

Objectives: Sensor networks constitute the backbone for the construction of personalized monitoring systems. Up to now, several sensor networks have been proposed for diverse pervasive healthcare applications, which are however characterized by a significant lack of open architectures, resulting in closed, non-interoperable and difficult to extend solutions. In this context, we propose an open and reconfigurable wireless sensor network (WSN) for pervasive health monitoring, with particular emphasis in its easy extension with additional sensors and functionality by incorporating embedded intelligence mechanisms. Methods: We consider a generic WSN architecture comprised of diverse sensor nodes (with communication and processing capabilities) and a mobile base unit (MBU) operating as the gateway between the sensors and the medical personnel, formulating this way a body area network (BAN). The primary focus of this work is on the intra-BAN data communication issues, adopting SensorML as the data representation mean, including the encoding of the monitoring patterns and the functionality of the sensor network. Results: In our prototype implementation two sensor nodes are emulated; one for heart rate monitoring and the other for blood glucose observations, while the MBU corresponds to a personal digital assistant (PDA) device. Java 2 Micro Edition (J2ME) is used to implement both the sensor nodes and the MBU components. Intra-BAN wireless communication relies on the Bluetooth protocol. Via an adaptive user interface in the MBU, health professionals may specify the monitoring parameters of the WSN and define the monitoring patterns of interest in terms of rules. Conclusions: This work constitutes an essential step towards the construction of open, extensible, inter - operable and intelligent WSNs for pervasive health monitoring.