Vassilis Kilintzis

Knowledge engineering for adverse drug event prevention

The primary aim of this work was the development of a uniform, contextualized and sustainable knowledge-based framework to support adverse drug event (ADE) prevention via Clinical Decision Support Systems (CDSSs). In this regard, the employed methodology involved first the systematic analysis and formalization of the knowledge sources elaborated in the scope of this work, through which an application-specific knowledge model has been defined. The entire framework architecture has been then specified and implemented by adopting Computer Interpretable Guidelines (CIGs) as the knowledge engineering formalism for its construction. The framework integrates diverse and dynamic knowledge sources in the form of rule-based ADE signals, all under a uniform Knowledge Base (KB) structure, according to the defined knowledge model. Equally important, it employs the means to contextualize the encapsulated knowledge, in order to provide appropriate support considering the specific local environment (hospital, medical department, language, etc.), as well as the mechanisms for knowledge querying, inference, sharing, and management. In this paper, we present thoroughly the establishment of the proposed knowledge framework by presenting the employed methodology and the results obtained as regards implementation, performance and validation aspects that highlight its applicability and virtue in medication safety.

From adverse drug event detection to prevention. A novel clinical decision support framework for medication safety.

BACKGROUND Errors related to medication seriously affect patient safety and the quality of healthcare. It has been widely argued that various types of such errors may be prevented by introducing Clinical Decision Support Systems (CDSSs) at the point of care. OBJECTIVES Although significant research has been conducted in the field, still medication safety is a crucial issue, while few research outcomes are mature enough to be considered for use in actual clinical settings. In this paper, we present a clinical decision support framework targeting medication safety with major focus on adverse drug event (ADE) prevention. METHODS The novelty of the framework lies in its design that approaches the problem holistically, i.e., starting from knowledge discovery to provide reliable numbers about ADEs per hospital or medical unit to describe their consequences and probable causes, and next employing the acquired knowledge for decision support services development and deployment. Major design features of the frameworks services are: a) their adaptation to the context of care (i.e. patient characteristics, place of care, and significance of ADEs), and b) their straightforward integration in the healthcare information technologies (IT) infrastructure thanks to the adoption of a service-oriented architecture (SOA) and relevant standards. RESULTS Our results illustrate the successful interoperability of the framework with two commercially available IT products, i.e., a Computerized Physician Order Entry (CPOE) and an Electronic Health Record (EHR) system, respectively, along with a Web prototype that is independent of existing healthcare IT products. The conducted clinical validation with domain experts and test cases illustrates that the impact of the framework is expected to be major, with respect to patient safety, and towards introducing the CDSS functionality in practical use. CONCLUSIONS This study illustrates an important potential for the applicability of the presented framework in delivering contextualized decision support services at the point of care and for making a substantial contribution towards ADE prevention. Nonetheless, further research is required in order to quantitatively and thoroughly assess its impact in medication safety.

WELCOME — Innovative integrated care platform using wearable sensing and smart cloud computing for COPD patients with Comorbidities

We propose WELCOME, an innovative integrated care platform using wearable sensors and smart cloud computing for Chronic Obstructive Pulmonary Disease (COPD) patients with co-morbidities. WELCOME aims to bring about a change in the reactive nature of the management of chronic diseases and its comorbidities, in particular through the development of a patient centred and proactive approach to COPD management. The aim of WELCOME is to support healthcare services to give early detection of complications (potentially reducing hospitalisations) and the prevention and mitigation of comorbidities (Heart Failure, Diabetes, Anxiety and Depression). The system incorporates patient hub, where it interacts with the patient via a light vest including a large number of non-invasive chest sensors for monitoring various relevant parameters. In addition, interactive applications to monitor and manage diabetes, anxiety and lifestyle issues will be provided to the patient. Informal carers will also be supported in dealing with their patients. On the other hand, welcome smart cloud platform is the heart of the proposed system where all the medical records and the monitoring data are managed and processed via the decision support system. Healthcare professionals will be able to securely access the WELCOME applications to monitor and manage the patients conditions and respond to alerts on personalized level.

Development and validation of the Greek version of the MNREAD acuity chart

The aim of this study was to develop MNREAD acuity charts in the Greek language (MNREAD‐GR) and establish their repeatability in a normal‐sighted population.

Agreement among three examiners of colour Doppler imaging retrobulbar blood flow velocity measurements

Purpose:  To assess the agreement among three masked examiners on central retinal artery (CRA) and ophthalmic artery (OA) blood flow velocity measurements performed with colour Doppler imaging (CDI) in healthy volunteers.

A reusable ontology for primitive and complex HL7 FHIR data types

HL7® FHIR® standard is a new standard aiming to offer more flexible interoperability mechanisms. We present a stand-alone RDF vocabulary as an OWL ontology that defines the primitive and complex data types of the FHIR framework, alongside their validation rules. We address the non-trivial questions of representing FHIR data types as RDF/OWL constructs in a coherent and complete manner. The proposed ontology can be used as a basic framework, where the complexity of a FHIR-based EHR is not required, while still maintaining semantic cohesion with an industrybased standard. It can also be the base for a complete representation of FHIR model as an ontology.

Evaluation of the performance of open-source RDBMS and triplestores for storing medical data over a web service.

An integral part of a system that manages medical data is the persistent storage engine. For almost twenty five years Relational Database Management Systems(RDBMS) were considered the obvious decision, yet today new technologies have emerged that require our attention as possible alternatives. Triplestores store information in terms of RDF triples without necessarily binding to a specific predefined structural model. In this paper we present an attempt to compare the performance of Apache JENA-Fuseki and the Virtuoso Universal Server 6 triplestores with that of MySQL 5.6 RDBMS for storing and retrieving medical information that it is communicated as RDF/XML ontology instances over a RESTful web service. The results show that the performance, calculated as average time of storing and retrieving instances, is significantly better using Virtuoso Server while MySQL performed better than Fuseki.

Novel Heidelberg retina tomograph-based morphological parameters derived from optic disc cupping surface processing.

PURPOSE To explore new features of the optic nerve head morphology using the Heidelberg retina tomograph (HRT) and to assess their discriminating power between glaucomatous patients and normal subjects. METHODS HRT reports, exported as TIFF images, from 97 normal subjects and 97 primary open-angle glaucoma (POAG) patients were used. For each image the contour of the dominant region of the optic disc cupping surface (dROCS) was transformed into a data series by calculating the distance of each contour pixel from the centroid. The length of contour (LC) and SD of contour (SDC) along with the dROCS area divided by the disc area (DA) HRT parameter were examined as novel parameters. RESULTS The means of LC and SDC, after adjustment for cup area (CA) and DA HRT parameters, and dROCS/DA, after adjustment for CA, presented statistically significant differences (ANCOVA, P < 0.001) between the two groups. Using LC and SDC together in discriminant analysis with leave-one-out cross-validation, 75.3% of cases were correctly classified. Using dROCS/DA together with SDC, the correct classification percentage was 80.6%. The area under the ROC curve was 0.782 for LC, 0.725 for SDC, 0.861 for dROCS/DA, and 0.879 for the linear discrimination function that combines dROCS/DA and SDC. CONCLUSIONS These findings suggest that LC, SDC, and dROCS/DA can be exploited to the discrimination between glaucomatous and normal subjects. LC and SDC seem to arise from the difference in the shape of the contour of dROCS between the groups, suggesting bigger deviations and irregularities in the POAG group.

Real-time Bite Detection from Smartwatch Orientation Sensor Data

The introduction of smartwatches over the last few years has made widely available a new type of wearable, everyday-usage device that is equipped with dozens of sensors. The sensors embedded in the smartwatch constitute a valuable source of data about the bodily functions of the smartwatch user; a source that has already been exploited for inferring information concerning the human behavior. One possible application of the aforementioned information inference is the prediction of eating-related events, such as bite instances. Accurate bite instance prediction from smartwatch sensors could serve as a trigger for appropriate user feedback in the context of just-in-time adaptive interventions for eating behavior management. In this paper, we present a novel method for real-time detection of bite instances from 3-axis orientation data acquired by a smartwatch. The evaluation of proposed method has been performed on eight annotated orientation timeseries, generated by eight individuals who wore a commercial smartwatch on their active hand while eating a bowl of milk with cereals. Both the classification accuracy of the method and its ability to make real-time decisions were evaluated, yielding very promising results.

Combining pervasive technologies and Cloud Computing for COPD and comorbidities management

Integrated care of patients with COPD and comorbidities requires the ability to regard patient status as a complex system. It can benefit from technologies that extract multiparametric information and detect changes in status along different axes. This raises the need for generation of systems that can unobtrusively monitor, compute, and combine multiorgan information. In this paper, the concept and ongoing work for such an approach is presented as regards the multiple types of data recorded, features extracted, and examples of how they are combined in the EU-funded project WELCOME (Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Comorbidities) [1], for the integrated management of COPD and comorbidities.