Charalampos Doulaverakis

GalenOWL: Ontology-based drug recommendations discovery

BackgroundIdentification of drug-drug and drug-diseases interactions can pose a difficult problem to cope with, as the increasingly large number of available drugs coupled with the ongoing research activities in the pharmaceutical domain, make the task of discovering relevant information difficult. Although international standards, such as the ICD-10 classification and the UNII registration, have been developed in order to enable efficient knowledge sharing, medical staff needs to be constantly updated in order to effectively discover drug interactions before prescription. The use of Semantic Web technologies has been proposed in earlier works, in order to tackle this problem.ResultsThis work presents a semantic-enabled online service, named GalenOWL, capable of offering real time drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards such as the aforementioned ICD-10 and UNII, provide the backbone of the common representation of medical data, while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. A comparison of the developed ontology-based system with a similar system developed using a traditional business logic rule engine is performed, giving insights on the advantages and drawbacks of both implementations.ConclusionsThe use of Semantic Web technologies has been found to be a good match for developing drug recommendation systems. Ontologies can effectively encapsulate medical knowledge and rule-based reasoning can capture and encode the drug interactions knowledge.

Association of global and local low endothelial shear stress with high-risk plaque using intracoronary 3D optical coherence tomography: Introduction of ‘shear stress score’

Aims The association of low endothelial shear stress (ESS) with high-risk plaque (HRP) has not been thoroughly investigated in humans. We investigated the local ESS and lumen remodelling patterns in HRPs using optical coherence tomography (OCT), developed the shear stress score, and explored its association with the prevalence of HRPs and clinical outcomes. Methods and results A total of 35 coronary arteries from 30 patients with stable angina or acute coronary syndrome (ACS) were reconstructed with three dimensional (3D) OCT. ESS was calculated using computational fluid dynamics and classified into low, moderate, and high in 3-mm-long subsegments. In each subsegment, (i) fibroatheromas (FAs) were classified into HRPs and non-HRPs based on fibrous cap (FC) thickness and lipid pool size, and (ii) lumen remodelling was classified into constrictive, compensatory, and expansive. In each artery the shear stress score was calculated as metric of the extent and severity of low ESS. FAs in low ESS subsegments had thinner FC compared with high ESS (89 ± 84 vs.138 ± 83 µm, P < 0.05). Low ESS subsegments predominantly co-localized with HRPs vs. non-HRPs (29 vs. 9%, P < 0.05) and high ESS subsegments predominantly with non-HRPs (9 vs. 24%, P < 0.05). Compensatory and expansive lumen remodelling were the predominant responses within subsegments with low ESS and HRPs. In non-stenotic FAs, low ESS was associated with HRPs vs. non-HRPs (29 vs. 3%, P < 0.05). Arteries with increased shear stress score had increased frequency of HRPs and were associated with ACS vs. stable angina. Conclusion Local low ESS and expansive lumen remodelling are associated with HRP. Arteries with increased shear stress score have increased frequency of HRPs and propensity to present with ACS.

IVUSAngio Tool

There is an ongoing research and clinical interest in the development of reliable and easily accessible software for the 3D reconstruction of coronary arteries. In this work, we present the architecture and validation of IVUSAngio Tool, an application which performs fast and accurate 3D reconstruction of the coronary arteries by using intravascular ultrasound (IVUS) and biplane angiography data. The 3D reconstruction is based on the fusion of the detected arterial boundaries in IVUS images with the 3D IVUS catheter path derived from the biplane angiography. The IVUSAngio Tool suite integrates all the intermediate processing and computational steps and provides a user-friendly interface. It also offers additional functionality, such as automatic selection of the end-diastolic IVUS images, semi-automatic and automatic IVUS segmentation, vascular morphometric measurements, graphical visualization of the 3D model and export in a format compatible with other computer-aided design applications. Our software was applied and validated in 31 human coronary arteries yielding quite promising results. Collectively, the use of IVUSAngio Tool significantly reduces the total processing time for 3D coronary reconstruction. IVUSAngio Tool is distributed as free software, publicly available to download and use.

An Approach to Intelligent Information Fusion in Sensor Saturated Urban Environments

This paper introduces a novel sensor information fusion system enabling security and surveillance in large scale sensor saturated urban environments. The system is built over state-of-the art sensor networks middleware and provides information fusion at multiple layers. A distinguishing characteristic of the system is that it support seamless integration with semantic web middleware (including ontologies and inference mechanisms), which enable intelligent high-level accurate reasoning. This is a key functionality for efficient surveillance in large scale environment, where manual inspection of individual tracking systems becomes extremely resourceful and overall impractical. A proof-of-concept implementation of the system manifests its benefits and technical challenges, while also outlining lessons learnt.

Panacea, a semantic-enabled drug recommendations discovery framework

BackgroundPersonalized drug prescription can be benefited from the use of intelligent information management and sharing. International standard classifications and terminologies have been developed in order to provide unique and unambiguous information representation. Such standards can be used as the basis of automated decision support systems for providing drug-drug and drug-disease interaction discovery. Additionally, Semantic Web technologies have been proposed in earlier works, in order to support such systems.ResultsThe paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standard classifications and terminologies, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on a lightweight ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome.ConclusionsPanacea is evaluated both in terms of quality of recommendations against real clinical data and performance. The quality recommendation gave useful insights regarding requirements for real world deployment and revealed several parameters that affected the recommendation results. Performance-wise, Panacea is compared to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modeling and approach to the problem, while also pinpointing the advantages of Panacea. Overall, the paper presents a framework for providing an efficient drug recommendations service where Semantic Web technologies are coupled with traditional business rule engines.

Intelligent Multi Sensor Fusion System for Advanced Situation Awareness in Urban Environments

This paper presents a distributed multi sensor data processing and fusion system providing sophisticated surveillance capabilities in the urban environment. The system enables visual/non-visual event detection, situation assessment, and semantic event-based reasoning for force protection and civil surveillance applications. The novelties lie in the high level system view approach, not only concentrating on data fusion methodologies per se, but rather on a holistic view of sensor data fusion that provides both lower (sensor) level and higher level (semantic) fusion. At the same time, we concentrate on easy and quick extensibility with new sensors and processing capabilities. The system also makes provisions for visualizing and processing space-time alerts from sensor detections up to high level alerts based on rule-based semantic reasoning over sensor data and fusion events. The proposed architecture has been validated in a number of different synthetic and live urban scenarios.

IVUS image processing and semantic analysis for Cardiovascular Diseases risk prediction

The work presented in this paper is part of a system able to perform risk classification of patients based on medical image analysis and on the semantically structured information of patient data from medical records and biochemical data. More specifically, the paper focuses on Intravascular Ultrasound (IVUS) image processing and the automated segmentation developed to extract the useful arterial boundaries. This is coupled with the design and implementation of a semantic reasoning-enabled knowledge base in OWL that integrates data from heterogeneous sources and incorporates functionality for DL classification. Performance evaluation of both IVUS image processing and knowledge base is discussed.

Applying SPARQL-Based Inference and Ontologies for Modelling and Execution of Clinical Practice Guidelines: A Case Study on Hypertension Management

Clinical practice guidelines (CPGs) constitute a systematically developed, critical body of medical knowledge which is compiled and maintained in order to assist healthcare professionals in decision making. They are available for diverse diseases/conditions and routinely used in many countries, providing reference material for healthcare delivery in clinical settings. As CPGs are paper-based, i.e. plain documents, there have been various approaches for their computerization and expression in a formal manner so that they can be incorporated in clinical information and decision support systems. Semantic Web technologies and ontologies have been extensively used for CPG formalization. In this paper, we present a novel method for the representation and execution of CPGs using OWL ontologies and SPARQL-based inference rules. The proposed approach is capable of expressing complex CPG constructs and can be used to express formalisms, such as negations, which are hard to express using ontologies alone. The encapsulation of SPARQL rules in the CPG ontology is based on the SPARQL Inference Notation (SPIN). The proposed representation of different aspects of CPGs, such as numerical comparisons, calculations, decision branches and state transitions, and their execution is demonstrated through the respective parts of comprehensive, though complex enough, CPGs for arterial hypertension management. The paper concludes by comparing the proposed approach with other relevant works, indicating its potential and limitations, as well as a future work directions.

A Visual Similarity Metric for Ontology Alignment

Ontology alignment is the process where two different ontologies that usually describe similar domains are ‘aligned’, i.e. a set of correspondences between their entities, regarding semantic equivalence, is determined. In order to identify these correspondences several methods have been proposed in literature. The most common features that these methods employ are string-, lexical-, structure- and semantic-based features for which several approaches have been developed. However, what hasn’t been investigated is the usage of visual-based features for determining entity similarity. Nowadays the existence of several resources that map lexical concepts onto images allows for exploiting visual features for this purpose. In this paper, a novel method, defining a visual-based similarity metric for ontology matching, is presented. Each ontological entity is associated with sets of images. State of the art visual feature extraction, clustering and indexing for computing the visual-based similarity between entities is employed. An adaptation of a Wordnet-based matching algorithm to exploit the visual similarity is also proposed. The proposed visual similarity approach is compared with standard metrics and demonstrates promising results.

A Framework for Automatic Detection of Lumen-Endothelium Border in Intracoronary OCT Image Sequences

Intracoronary optical coherence tomography (OCT) is increasingly being used for real-time visualization of coronary arteries aiming to help in the identification of high-risk atherosclerotic plaques associated with geometrical and morphological features of the arterial wall. This paper presents a framework towards the automatic detection of the inner wall of the coronary artery (lumen-endothelium border) in intracoronary OCT image sequences by employing a multi-step image processing method. The major focus of this work was to address difficult cases that are frequently met in intracoronary OCT, e.g. images with small/big branches, multiple branches, blood presence, calcifications, artifacts, etc. We present each step employed and the results obtained both in qualitative and quantitative terms. The proposed segmentation algorithm has been proven very efficient in the majority of the examined cases.