What the newspapers say about medication adherence:a content analysis

Background - This study investigates the coverage of adherence to medicine by the UK and US newsprint media. Adherence to medicine is recognised as an important issue facing healthcare professionals and the newsprint media is a key source of health information, however, little is known about newspaper coverage of medication adherence. Methods - A search of the newspaper database Nexis®UK from 2004–2011 was performed. Content analysis of newspaper articles which referenced medication adherence from the twelve highest circulating UK and US daily newspapers and their Sunday equivalents was carried out. A second researcher coded a 15% sample of newspaper articles to establish the inter-rater reliability of coding. Results - Searches of newspaper coverage of medication adherence in the UK and US yielded 181 relevant articles for each country. There was a large increase in the number of scientific articles on medication adherence in PubMed® over the study period, however, this was not reflected in the frequency of newspaper articles published on medication adherence. UK newspaper articles were significantly more likely to report the benefits of adherence (p = 0.005), whereas US newspaper articles were significantly more likely to report adherence issues in the elderly population (p = 0.004) and adherence associated with diseases of the central nervous system (p = 0.046). The most commonly reported barriers to adherence were patient factors e.g. poor memory, beliefs and age, whereas, the most commonly reported facilitators to adherence were medication factors including simplified regimens, shorter treatment duration and combination tablets. HIV/AIDS was the single most frequently cited disease (reported in 20% of newspaper articles). Poor quality reporting of medication adherence was identified in 62% of newspaper articles. Conclusion - Adherence is not well covered in the newspaper media despite a significant presence in the medical literature. The mass media have the potential to help educate and shape the public’s knowledge regarding the importance of medication adherence; this potential is not being realised at present.

Originator or propagator? Incorporating social role theory into topic models for twitter content analysis

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently in different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles. Copyright 2013 ACM.

Using XML and XSLT for flexible elicitation of mental-health risk knowledge

Current tools for assessing risks associated with mental-health problems require assessors to make high-level judgements based on clinical experience. This paper describes how new technologies can enhance qualitative research methods to identify lower-level cues underlying these judgements, which can be collected by people without a specialist mental-health background. Content analysis of interviews with 46 multidisciplinary mental-health experts exposed the cues and their interrelationships, which were represented by a mind map using software that stores maps as XML. All 46 mind maps were integrated into a single XML knowledge structure and analysed by a Lisp program to generate quantitative information about the numbers of experts associated with each part of it. The knowledge was refined by the experts, using software developed in Flash to record their collective views within the XML itself. These views specified how the XML should be transformed by XSLT, a technology for rendering XML, which resulted in a validated hierarchical knowledge structure associating patient cues with risks. Changing knowledge elicitation requirements were accommodated by flexible transformations of XML data using XSLT, which also facilitated generation of multiple data-gathering tools suiting different assessment circumstances and levels of mental-health knowledge. © 2007 Informa UK Ltd All rights reserved.

Incorporating social role theory into topic models for social media content analysis

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

An automated tool for semantic accessing to formal software models

Many software engineers have found that it is difficult to understand, incorporate and use different formal models consistently in the process of software developments, especially for large and complex software systems. This is mainly due to the complex mathematical nature of the formal methods and the lack of tool support. It is highly desirable to have software models and their related software artefacts systematically connected and used collaboratively, rather than in isolation. The success of the Semantic Web, as the next generation of Web technology, can have profound impact on the environment for formal software development. It allows both the software engineers and machines to understand the content of formal models and supports more effective software design in terms of understanding, sharing and reusing in a distributed manner. To realise the full potential of the Semantic Web in formal software development, effectively creating proper semantic metadata for formal software models and their related software artefacts is crucial. This paper proposed a framework that allows users to interconnect the knowledge about formal software models and other related documents using the semantic technology. We first propose a methodology with tool support is proposed to automatically derive ontological metadata from formal software models and semantically describe them. We then develop a Semantic Web environment for representing and sharing formal Z/OZ models. A method with prototype tool is presented to enhance semantic query to software models and other artefacts. © 2014.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Reliability modelling and verification of manufacturing processes based on process knowledge management

Reliability modelling and verification is indispensable in modern manufacturing, especially for product development risk reduction. Based on the discussion of the deficiencies of traditional reliability modelling methods for process reliability, a novel modelling method is presented herein that draws upon a knowledge network of process scenarios based on the analytic network process (ANP). An integration framework of manufacturing process reliability and product quality is presented together with a product development and reliability verification process. According to the roles of key characteristics (KCs) in manufacturing processes, KCs are organised into four clusters, that is, product KCs, material KCs, operation KCs and equipment KCs, which represent the process knowledge network of manufacturing processes. A mathematical model and algorithm is developed for calculating the reliability requirements of KCs with respect to different manufacturing process scenarios. A case study on valve-sleeve component manufacturing is provided as an application example of the new reliability modelling and verification procedure. This methodology is applied in the valve-sleeve component manufacturing processes to manage and deploy production resources.

Decision-making in product quality based on failure knowledge

Decision-making in product quality is an indispensable stage in product development, in order to reduce product development risk. Based on the identification of the deficiencies of quality function deployment (QFD) and failure modes and effects analysis (FMEA), a novel decision-making method is presented that draws upon a knowledge network of failure scenarios. An ontological expression of failure scenarios is presented together with a framework of failure knowledge network (FKN). According to the roles of quality characteristics (QCs) in failure processing, QCs are set into three categories namely perceptible QCs, restrictive QCs, and controllable QCs, which present the monitor targets, control targets and improvement targets respectively for quality management. A mathematical model and algorithms based on the analytic network process (ANP) is introduced for calculating the priority of QCs with respect to different development scenarios. A case study is provided according to the proposed decision-making procedure based on FKN. This methodology is applied in the propeller design process to solve the problem of prioritising QCs. This paper provides a practical approach for decision-making in product quality. Copyright © 2011 Inderscience Enterprises Ltd.

Discovering knowledge structures in mind maps of mental health risks

This thesis addressed the problem of risk analysis in mental healthcare, with respect to the GRiST project at Aston University. That project provides a risk-screening tool based on the knowledge of 46 experts, captured as mind maps that describe relationships between risks and patterns of behavioural cues. Mind mapping, though, fails to impose control over content, and is not considered to formally represent knowledge. In contrast, this thesis treated GRiSTs mind maps as a rich knowledge base in need of refinement; that process drew on existing techniques for designing databases and knowledge bases. Identifying well-defined mind map concepts, though, was hindered by spelling mistakes, and by ambiguity and lack of coverage in the tools used for researching words. A novel use of the Edit Distance overcame those problems, by assessing similarities between mind map texts, and between spelling mistakes and suggested corrections. That algorithm further identified stems, the shortest text string found in related word-forms. As opposed to existing approaches’ reliance on built-in linguistic knowledge, this thesis devised a novel, more flexible text-based technique. An additional tool, Correspondence Analysis, found patterns in word usage that allowed machines to determine likely intended meanings for ambiguous words. Correspondence Analysis further produced clusters of related concepts, which in turn drove the automatic generation of novel mind maps. Such maps underpinned adjuncts to the mind mapping software used by GRiST; one such new facility generated novel mind maps, to reflect the collected expert knowledge on any specified concept. Mind maps from GRiST are stored as XML, which suggested storing them in an XML database. In fact, the entire approach here is ”XML-centric”, in that all stages rely on XML as far as possible. A XML-based query language allows user to retrieve information from the mind map knowledge base. The approach, it was concluded, will prove valuable to mind mapping in general, and to detecting patterns in any type of digital information.