Heart failure ontology

Abstract Ontology represents explicit specification of knowledge in a specific domain of interest in the form of concepts and relations among them. This paper presents a medical ontology describing the domain of heart failure (HF). Construction of ontology for a domain like HF is recognized as an important step in systematization of existing medical knowledge. The main virtue of ontology is that the represented knowledge is both computer and humanreadable. The current development of the HF ontology is one of the main results of the EU Heartfaid project. The ontology has been implemented using Ontology Web Language and Protégé editing tool. It consists of roughly 200 classes, 100 relations and 2000 instances. The ontology is a precise, voluminous, portable, and upgradable representation of the HF domain. It is also a useful framework for building knowledge based systems in the HF domain, as well as for unambiguous communication between professionals. In the process of developing the HF ontology there have been significant technical and medical dilemmas. The current result should not be treated as the ultimate solution but as a starting point that will stimulate further research and development activities that can be very relevant for both intelligent computer systems and precise communication of medical knowledge

A Hyper-Solution Framework for SVM Classification: Application for Predicting Destabilizations in Chronic Heart Failure Patients

Support Vector Machines (SVMs) represent a powerful learning paradigm able to provide accurate and reliable decision functions in several application fields. In particular, they are really attractive for application in medical domain, where often a lack of knowledge exists. Kernel trick, on which SVMs are based, allows to map non-linearly separable data into potentially linearly separable one, according to the kernel function and its internal parameters value. During recent years non-parametric approaches have also been proposed for learning the most appropriate kernel, such as linear combination of basic kernels. Thus, SVMs classifiers may have several parameters to be tuned and their optimal values are usually difficult to be identified a-priori. Furthermore, combining different classifiers may reduce risk to perform errors on new unseen data. For such reasons, we present an hyper-solution framework for SVM classification, based on meta-heuristics, that searches for the most reliable hyper-classifier (SVM with a basic kernel, SVM with a combination of kernel, and ensemble of SVMs), and for its optimal configuration. We have applied the proposed framework on a critical and quite complex issue for the management of Chronic Heart Failure patient: the early detection of decompensation conditions. In fact, predicting new destabilizations in advance may reduce the burden of heart failure on the healthcare systems while improving quality of life of affected patients. Promising reliability has been obtained on 10-fold cross validation, proving our approach to be efficient and effective for an high-level analysis of clinical data

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu

Ontology for Psychophysiological Dysregulation of Anger/Aggression

The advancement of Information Technology in the last four decades led to the use of computers in medicine. A new area called Medical Informatics has emerged. This area comprises the application of IT to healthcare with the aim of creating tools that help healthcare personnel diagnose and treat patients more accurately and efficiently. IT not only provides tools for storing, integrating, and updating patient information base but also for processing information efficiently. One of such tools is a Clinical Decision Support System. Ontologies are an integral part of clinical decision support systems because they help formalize and integrate domain knowledge. In this project, we developed a software program that assists clinicians in making diagnostic decisions about a particular problem type called ‘psychophysiological dysregulation of anger/aggression’. We created a new ontology for the problem domain. The computer program asks a set of pertinent questions and the patient or clinician on behalf of the patient is required to answer it. All these answers along with the results from various lab assessment tests are fed into the software program which then outputs a diagnosis by interacting with the ontology and also proposes the preferred treatment plan. While undergoing the treatment the patient is monitored at regular intervals by the clinician and this data is recorded as the treatment episode data. The tools and technologies used for this project are Web Ontology Language (OWL) version 2, Protégé 4.1.0 Beta, Java, Eclipse Helios IDE and IBM DB2. Adviser: Jitender S. Deogu

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

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Strategic Intelligence Monitor on Personal Health Systems (SIMPHS): Market Structure and Innovation Dynamics

Personal Health Systems (PHS) and Remote Patient Monitoring and Treatment (RMT) have the potential to alter the way healthcare is provided by increasing the quantity and quality of care. This report explores the current status of PHS and, more specifically of the RMT market in Europe. It addresses the question of how these technologies can contribute facing some of the challenges standing in front of the European healthcare delivery systems causes by higher demand pressures through chronic diseases and demographic change combined with diminishing resources for health care. An uptake and diffusion of these services would potentially lead to benefits through a reduction in death rates, and avoid recurring hospitalisation in a cost-effective manner. Yet the report identifies different categories of barriers hampering a full deployment of RMT in Europe. In the concluding part the reports provides a number of tentative policy options specifically aimed at fostering EU-wide deployment of RMT/PHS.JRC.DDG.J.4-Information Societ

Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach

Cognitive assisted living ambient system: a survey

The demographic change towards an aging population is creating a significant impact and introducing drastic challenges to our society. We therefore need to find ways to assist older people to stay independently and prevent social isolation of these population. Information and Communication Technologies (ICT) provide various solutions to help older adults to improve their quality of life, stay healthier, and live independently for a time. Ambient Assisted Living (AAL) is a field to investigate innovative technologies to provide assistance as well as healthcare and rehabilitation to impaired seniors. The paper provides a review of research background and technologies of AAL