Teresa Meneu

Activity-based Process Mining for Clinical Pathways Computer aided design

Current trends in health management improvement demand the standardization of care protocols to achieve better quality and efficiency. The use of Clinical Pathways is an emerging solution for that problem. However, current Clinical Pathways are big manuals written in natural language and highly affected by human subjectivity. These problems make the deployment and dissemination of them extremely difficult in real practice environments. In this work, a complete computer based architecture to help the representation and execution of Clinical Pathways is suggested. Furthermore, the difficulties inherent to the design of formal Clinical Pathways in this way requires new specific design tools to help making the system useful. Process Mining techniques can help to automatically infer processes definition from execution samples. Yet, the classical Process Mining paradigm is not totally compatible with the Clinical Pathways paradigm. In this paper, a pattern recognition algorithm based in an evolution of the Process Mining classical paradigm is presented and evaluated as a solution to this situation. The proposed algorithm is able to infer Clinical Pathways from execution logs to support the design of Clinical Pathways.

New technologies for promoting a healthy diet and active living.

Information and communication technologies (ICT) offer innovative formats for promoting healthy lifestyles and reinforcing public health initiatives. They can be applied to large population segments without losing the functionality of being tailored to individual fluctuating needs. Advantages of ICT include real-time provision and adaptation of nutrition and health recommendations based on an individuals particular situation, the potential to combine assessment procedures with healthy lifestyle support and the ability to unify psychosocial and cultural dimensions to enhance adherence. Two pilot programs are presented that show the potential for applying ICT to the promotion of healthy eating and physical activity habits.

Applying evidence-based medicine in telehealth: an interactive pattern recognition approximation.

Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.

Integrated and Personalised Risk Management in the Sensing Enterprise

Due to its impact on economy, resources, environment and society, manufacturing is of strategic value to Europe. European manufacturing has to embrace a new logic of global socioeconomic sustainability, in which it addresses not only the welfare of its population, but also of emerging economies, contributing at the same time to the preservation of the environment and the resources. Megatrends that have a considerable impact on European manufacturing are:

Magnetic resonance imaging postprocessing techniques in the study of brain connectivity

Abstract Brain connectivity is a key concept for understanding brain function. Current methods to detect and quantify different types of connectivity with neuroimaging techniques are fundamental for understanding the pathophysiology of many neurologic and psychiatric disorders. This article aims to present a critical review of the magnetic resonance imaging techniques used to measure brain connectivity within the context of the Human Connectome Project. We review techniques used to measure: a) structural connectivity b) functional connectivity (main component analysis, independent component analysis, seed voxel, meta-analysis), and c) effective connectivity (psychophysiological interactions, causal dynamic models, multivariate autoregressive models, and structural equation models). These three approaches make it possible to combine and use different statistical techniques to elaborate mathematical models in the attempt to understand the functioning of the brain. The findings obtained with these techniques must be validated by other techniques for analyzing structural and functional connectivity. This information is integrated in the Human Connectome Project where all these approaches converge to provide a representation of all the different models of connectivity.

Life Assistance Protocols (LAP) A Model for the Personalization of Lifestyle Support for Health and Wellbeing

The WHO Report 2002 describes in detail how a few major risk factors account for a significant proportion of all deaths and diseases in most countries. Those risk factors are directly related with lifestyle and unhealthy habits. Thus, population based approaches addressing lifestyle factors have huge potential for the prevention of severe morbidity and mortality. The best therapeutic choice is guided by correct risk stratification for the individual, taking into account all the risk factors, not only the physio-pathological ones, but also the genetic, environmental, socio-cultural, behavioural and personal factors, in order to personalize health-hygiene measures on the basis of the risk profile. Health risk management programs address the identified risk levels of each individual and the action strategy proposed to elaborate a complete comprehensive program of life which should lead him from the current status towards the optimal one. Therefore, the objective of our proposed model has been to develop an intelligent system for the planning of personalized health risk management processes considering both the health strategies and the personal preferences: The Life Assistance Protocols (LAPs).

Heart Cycle: Facilitating the deployment of advanced care processes

Current trends in health management improvement demand the standardization of care protocols to achieve better quality and efficiency. The use of Clinical Pathways is an emerging solution for that problem. However, current Clinical Pathways are big manuals written in natural language and highly affected by human subjectivity. These problems make their deployment and dissemination extremely difficult in real practice environments. Furthermore, the intrinsic difficulties for the design of formal Clinical Pathways requires new specific design tools to help making them relly useful and cost-effective. Process Mining techniques can help to automatically infer processes definition from execution samples and, thus, support the automatization of the standardization and continuous control of healthcare processes. This way, they can become a relevant helping tool for clinical experts and healthcare systems for reducing variability in clinical practice and better understand the performance of the system.

Técnicas de análisis de posproceso en resonancia magnetica para el estudio de la conectividad cerebral

Brain connectivity is a key concept for understanding brain function. Current methods to detect and quantify different types of connectivity with neuroimaging techniques are fundamental for understanding the pathophysiology of many neurologic and psychiatric disorders. This article aims to present a critical review of the magnetic resonance imaging techniques used to measure brain connectivity within the context of the Human Connectome Project. We review techniques used to measure: a) structural connectivity b) functional connectivity (main component analysis, independent component analysis, seed voxel, meta-analysis), and c) effective connectivity (psychophysiological interactions, causal dynamic models, multivariate autoregressive models, and structural equation models). These three approaches make it possible to combine and use different statistical techniques to elaborate mathematical models in the attempt to understand the functioning of the brain. The findings obtained with these techniques must be validated by other techniques for analyzing structural and functional connectivity. This information is integrated in the Human Connectome Project where all these approaches converge to provide a representation of all the different models of connectivity.

Evaluating Mobility Impact on Wireless Sensor Network

An increasing number of applications based on Wireless Sensor Networks assume mobile environments(Mobile WSNs). Mobile WSNs propose several converging issues with Mobile Ad-hoc Networks (MANETs) but the peculiarities of their technology and application domain advise a specific theoretical analysis of mobility impact on network connectivity. Network performance depends by several factors; there is a clear relationship between overall performance and the efficiency of network mechanisms (e.g. topology control and routing) that are directly affected by network connectivity. The paper first proposes an evaluation of randomly deployed clustered WSNs in function of network size/density, topology and communication range. Then, the mobility impact on network connectivity is analyzed and evaluated extending the analysis to overlay configuration. This evaluation has an implicit relationship with mobile behaviors. In order to provide extended analysis capabilities, an analytic model for mobile behaviors is also proposed. All reported results were obtained through simulations according to a general approach, independent from routing protocols or any other domain specific mechanisms as well as by environmental conditions.

An integrated advanced communication and coaching platform for enabling personalized management of chronic cardiovascular diseases

Chronic cardiovascular diseases directly account for millions of deaths, billions of Euros and a big number of disabilities affecting the worlds population. Even though primary and secondary prevention factors are well known, the awareness and the concern of citizens and patients is not big enough to cause a significant change in lifestyle that modifies the increasing trends. Patients and families, professionals and healthcare systems are not prepared to fight against this burden in an effective and aligned way. Some disease management programmes based on ICT solutions have and are currently being tested around the world but their relative impaction has been very limited. This paper proposes a new turn into Personal Health Systems applied to chronic disease management by increasing the capabilities for personalization, providing the patients with motivation and coaching support and enabling the work of the professionals with intelligent tools for strategic and clinical decision making based on the newest medical evidence.