Integrated System for Continuous Monitoring of COPD

Chronic Obstructive Pulmonary Disease (COPD) is a progressive pulmonary disease characterized by reduction in airflow and is not fully reversible. COPD is the major cause of mortality and increased levels of disability, particularly in the elderly. Symptoms vary among individuals and include breathlessness, dyspnea, abnormal sputum and chronic cough. Exposure to tobacco smoke is by far the most important risk factor in the development of COPD and is associated with high levels of morbidity and mortality. In this chapter the authors will present a system for the integrated care of COPD focusing on prevention and intervention.

Assistive Technologies and Computers

The International Classification of Functioning, Disability and Health (ICF) (WHO, 2001) provides the term disability as a part of the multi-aspects of the interaction between the individual and social environmental context in which it operates. Therefore a disabled person is a person who has impairment, activity limitation or participation restriction. A person in this situation can present difficulties in occupational performance. It is necessary to develop a set of resources, technological or otherwise, to offset these difficulties, decrease the distance between exclusion and participation and contribute to the integration of people with functional diversity in society. These resources are called support products or technology support, but do not eliminate the deficits, they can eliminate the limitation of the performance of persons with disabilities.

Assessment of Chronic Health Care through an Internet Consensus Tool

“Health Consensus for the Assessment of Chronic Care Programs” (HC-ACP) is an internet based application created to promote and facilitate the participation of health professionals in the definition of a set of indicators for the assessment of chronic care and management of areas of improvement in this field. The first prototype of the application has been applied twice, first in the region of Catalonia, and in a second project in the context of the whole Spanish Health System. HC-ACP has collected contributions from more than 800 health professionals from around Spain including profiles in the fields of management, health care professional, health planning and quality assessment, allowing sharing and aggregate knowledge and clinical experience from a wide range of points of view. After a process of literature review and panel meetings with professionals who proposed a wide list of indicators, the HC-ACP application was used to select a minimum set of indicators following a participative process based on Health Consensus, an online Real Time Delphi method. The first part of this chapter is devoted to expose paradigms that define the interdisciplinary research field of the method, the second part of the chapter presents the Health Consensus method, and finally the third part exposes a detailed description of the HC-ACP application and the followed process. Besides the relevance and utility of the Health Consensus method, the action-research conducted to build the application proves the efficiency and effectiveness of getting health professionals really involved in the processes of defining the models to assess the healthcare system. The online method proposed has been accepted by participants who have expressed high levels of satisfaction during the participation process.

Clinical Practice Ontology Automatic Learning from SOAP Reports

We show how we implemented an end-to-end process to automatically develop a clinical practice knowledge base acquiring from SOAP notes. With our contribution we intend to overcome the “Knowledge Acquisition Bottleneck” problem by jump-starting the knowledge gathering from the most widely available source of clinical information that are natural language reports. We present the different phases of our process to populate automatically a proposed ontology with clinical assertions extracted from daily routine SOAP notes. The enriched ontology becomes a reasoning able knowledge base that depicts accurately and realistically the clinical practice represented by the source reports. With this knowledge structure in place and novel state-of-the-art reasoning capabilities, based in consequence driven reasoners, a clinical QA system based in controlled natural language is introduced that reveals breakthrough possibilities regarding the applicability of Artificial Intelligence techniques to the medical field.

Analysis of 3D Corpus Callosum Images in the Brains of Autistic Individuals

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is characterized by abnormalities in behavior and higher cognitive functions. The corpus callosum (CC) is the largest fiber bundle that connects the left and the right cerebral hemispheres of the human brain. Several studies have revealed an abnormal anatomy of the CC in the brains of autistic individuals that associates this neurodevelopmental condition with impaired communication between the hemispheres. In this chapter, we develop a framework to analyze the CC of autistic individuals in order to provide a diagnostic tool for autism. The key advantage of this approach is the development of a cylindrical mapping that offers simplified coordinates for comparing the brains of autistic individuals and neurotypicals. Experimental results showed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu, and body of their CCs. Moreover, the initial centerline-based shape analysis of the CC documented a promising supplement to the current techniques for diagnosing autism.

Automated Categorisation of Nailfold Capillaroscopy Images

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold. It is particularly useful for early detection of scleroderma spectrum disorders and evaluation of Raynaud's phenomenon. While automated approaches to analysing NC images are relatively rare, they are typically based on extraction and analysis of individual capillaries from the images in order to assign a patient to one of the commonly employed scleroderma patterns. In this chapter, we present a different approach that does not rely on individual capillaries but performs interpretation in a holistic way based on information gathered from an image or a selected image region. In particular, our algorithm employs texture analysis to characterise the underlying patterns, coupled with a classification stage to first identify patterns in fingers, and then, through a voting strategy, reach a decision for a patient. Experimental results on a set of NC images with known ground truth demonstrate the efficacy of the proposed approach.

Alzheimer's Electroencephalogram Event Scalp and Source Localization

Alzheimer's disease is the most common cause of dementia which causes a progressive and irreversible impairment of several cognitive functions. The aging population has been increasing significantly in recent decades and this disease affects mainly the elderly. Its diagnostic accuracy is relatively low and there is not a biomarker able to detect AD without invasive tests. Despite the progress in better understanding the disease there remains no prospect of cure at least in the near future. The electroencephalogram (EEG) test is a widely available technology in clinical settings. It may help diagnosis of brain disorders, once it can be used in patients who have cognitive impairment involving a general decrease in overall brain function or in patients with a located deficit. This study is a new approach to improve the scalp localization and the detection of brain anomalies (EEG temporal events) sources associated with AD by using the EEG.

Definition of a Pervasive Architecture for a Central Monitoring of Homecare Systems

Every year the queues in hospitals publics and privates grows due to, among others, the increasing of the world population and the delay in the patients service. This is a serious problem faced by administrators of hospitals, which believe that it is increasingly difficult to offer a service of quality to those who search for them. One of the ways to decrease these queues is through the development of homecare systems that allow the patient to receive the clinic treatment directly in his house. The development of these kinds of systems would help to decrease the queues and consequently, would improve the attendance of those who goes to the hospitals looking for assistance. Considering this, this work has as main purpose to present the architecture modeling of a pervasive system to be applied in homecare environments. The pervasive systems developed from this modeling aim to improve the services provided by healthcare professionals in the treatment of patients that are located in their houses. The architecture proposed by the methodology uses concepts of pervasive computing to provide access to information any- time and wherever the user is, once that a homecare environment has a high level of dynamicity. The knowledge representation of the homecare environment needed in the modeling of the architecture is made through ontologies due to the possibility of reuse of the information stored, as well as the interoperability of information among different computational devices. To validate the proposed methodology, we present two use cases, which are also used to demonstrate the workflow of the pervasive system of homecare.

Advanced Approaches to Diagnose and Treat the Chronic Autoimmune Disorders

Pathology and dynamics of particular cells and the molecular components of immune system is still challenging to be traced within living organisms. The techniques of molecular imaging (MI) are promising tools to monitor the immune system at work, to improve or allow personalized diagnostics and treatment, especially of the autoimmune diseases. In this study some possible targets for MI and biosensing are discussed. The personalized medicine, in addition to bioinformatics-based systemic approach, requires extensive research and novel high-throughput technologies like next generation of imaging, biosensing experimental systems based on microfluidics, nanotechnology, femtochemistry, superresolution (STED, STORM, PALM, SOFI, etc.), label-free imaging, spectroscopy (including TCSPC), MRI, multimodal optical methods, accoustic imaging through ultrasonic waves, nuclear medicine methods like SPECT and PET. Moreover, dedicated designs of modular Lab-on-Chip solutions are of high demand to perform multipurpose cell measurement and give a possibility to flexibly interact with sensed objects.

Trends and New Advances on Wearable and Mobile Technologies for Parkinson's Disease Monitoring and Assessment of Motor Symptoms

The aim of this work is to analyze the trends and new advances carried out in the last decades in the field of Parkinson's disease monitoring and management and more specifically regarding wearable and mobile technologies. The challenges of such technologies is to monitor, to assess and to manage the full range of PD symptoms through monitoring and testing routines while not hampering the patient's daily activities, identifying the correlation between the different dimensions affecting the severity of symptoms and the evolution of the disease and enabling the clinician to manage more efficiently the patient by providing timely indications on the effectiveness of the therapy and suggestions on therapy changes.