Capturing Scientific Knowledge on Medical Risk Factors

In this paper, we describe a model for representing scientific knowledge of risk factors in medicine in an explicit format which enables its use for automated reasoning. The resulting model supports linking the conclusions of up-to-date clinical research with data relating to individual patients. This model, which we have implemented as an ontology-based system using Linked Data, enables the capture of risk factor knowledge and serves as a translational research tool to apply that knowledge to assist with patient treatment, lifestyle, and education. Knowledge captured using this model can be disseminated for other intelligent systems to use for a variety of purposes, for example, to explore the state of the available medical knowledge

Are you thinking what I'm thinking? Representing Metacognition with Question­-based Dialogue

In the following paper, we present Noracle, a tool for creating representational artefacts of metacognitive thinking in a collaborative, social environment. The tool uses only question asking, rather than the typical question/answer paradigm found in threaded discussions, as a mechanism for supporting awareness and reflection on metacognitive activity, and for supporting self- regulated learning. The weblike artefact produced by learner contributions is intended to support learners in mapping a given domain, identifying points of convergence and recognizing gaps in the knowledge representation. In this paper, the authors present the model of the tool, a use case scenario and a discussion of the opportunities and limitations related to this approach

Social personalized adaptive e-learning environment : Topolor - implementation and evaluation

This paper presents a quantitative study on the use of Topolor - a prototype that introduces Web 2.0 tools and Facebook-like appearance into an adaptive educational hypermedia system. We present the system design and its evaluation using system usability scale questionnaire and learning behavior data analysis. The results indicate high level of student satisfaction with the learning experience and the diversity of learning activities

ACQUA: Automated Community-based Question Answering through the Discretisation of Shallow Linguistic Features

This paper addresses the problem of determining the best answer in Community-based Question Answering (CQA) websites by focussing on the content. In particular, we present a novel system, ACQUA (http://acqua.kmi.open.ac.uk), that can be installed onto the majority of browsers as a plugin. The service offers a seamless and accurate prediction of the answer to be accepted. Our system is based on a novel approach for processing answers in CQAs. Previous research on this topic relies on the exploitation of community feedback on the answers, which involves rating of either users (e.g., reputation) or answers (e.g. scores manually assigned to answers). We propose a new technique that leverages the content/textual features of answers in a novel way. Our approach delivers better results than related linguistics-based solutions and manages to match rating-based approaches. More specifically, the gain in performance is achieved by rendering the values of these features into a discretised form. We also show how our technique manages to deliver equally good results in real-time settings, as opposed to having to rely on information not always readily available, such as user ratings and answer scores. We ran an evaluation on 21 StackExchange websites covering around 4 million questions and more than 8 million answers. We obtain 84% average precision and 70% recall, which shows that our technique is robust, effective, and widely applicable

CoPe_it! - Supporting collaboration, enhancing learning

CoPe_it! is an innovative web-based tool that complies with collaborative practices to provide members of communities with the appropriate means to manage individual and collective knowledge, and collaborate towards the solution of diverse issues. In this article, we demonstrate its applicability in tackling data-intensive collaboration settings, which are characterized by big volumes of complex and interrelated data obtained from diverse sources, and knowledge expressed by diverse participants. We focus on issues related to the representation of such settings and the proposed approach towards making it easier for participants to follow the evolution of a collaboration, comprehend it in its entirety, and meaningfully aggregate data in order to resolve the issue under consideration

Cyber-physical Threat Detection Platform Designed for Healthcare Systems

Hospitals are responsible for delivering healthcare services to patients in need. These services are large and complex and get affected by multiple interacting actors, such as doctors, nurses, patients, citizens, medical suppliers, health insurance providers. Lately, hospitals around the world are one of the main targets when it comes to terrorist attacks, the cyber realm being the principal source. The healthcare sector is particularly vulnerable due to heavy involvement in patient personal and health information, time constraints, and complex day-to-day operations. In addition to cyber-threats, physical threats are increasingly growing and even healthcare facilities are not immune to them. Malicious intended people created cyber threatening attacks with the purpose to systematically collect evidence against the healthcare system, to advocate for the end of such attacks, and to endanger people\u27s lives or to use the stolen personal data for bad intended actions. Henceforth it is necessary to build a platform that will get alerts and incidents at a fast pace in real-time to prevent any casualties at low cost. SAFECARE project aims to offer protection to hospitals and increase the compliance for the European regulations and security regarding ethics and privacy for health services. This paper presents a solution that will enhance security in hospitals. The primary platform will be built based on a BTMS (Building Threat Monitoring System) where events, incidents, and alerts will be transmitted by sensors from hospital rooms in real-time. Several scenarios were thought to simulate different types of attacks against hospitals and according to the scenarios, various prototypes will be built for assuring the security of the personal and patients from various hospitals.</p

Socioeconomic Crisis and Incidence of Acute Myocardial Infarction in Messinia, Greece

In the last 5 years Greece is facing the worst socioeconomic crisis since the end of the Second World War. The purpose of the current study was to gather all the incident cases of acute myocardial infarction (AMI) that were hospitalized in the General Hospital of Kalamata during the last 10 years. Our results suggest that the prolonged financial crisis may have led to a higher incidence of AMI in the population of Messinia, Greece

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase&nbsp;1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation&nbsp;disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age&nbsp; 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score&nbsp; 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc&nbsp;= 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N&nbsp;= 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in&nbsp;Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in&nbsp;Asia&nbsp;and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Observations on Reversible Addition Fragmentation Chain Transfer (RAFT) Polymerisations in Solution and Emulsion

We introduce a geolocation-aware semantic annotation model that extends the existing solutions for spotting and disambiguation of places within user-generated texts. The implemented prototype processes the text of weblog posts and annotates the places and toponyms. It outperforms existing solutions by taking into consideration the embedded geolocation data. The evaluation of the model is based on a set of randomly selected 3,165 geolocation embedded weblog posts, obtained from 1,775 web feeds. The results demonstrate a high degree of accuracy in annotation (87.7%) and a considerable gain (27.8%) in identifying additional entities, and therefore support the adoption of the model for supplementing the existing solutions