The evidence-based development of an intervention to address the information needs of adults newly diagnosed with primary brain tumours and their carers

Adults diagnosed with primary brain tumours often experience physical, cognitive and neuropsychiatric impairments and decline in quality of life. Although disease and treatment-related information is commonly provided to cancer patients and carers, newly diagnosed brain tumour patients and their carers report unmet information needs. Few interventions have been designed or proven to address these information needs. Accordingly, a three-study research program, that incorporated both qualitative and quantitative research methods, was designed to: 1) identify and select an intervention to improve the provision of information, and meet the needs of patients with a brain tumour; 2) use an evidence-based approach to establish the content, language and format for the intervention; and 3) assess the acceptability of the intervention, and the feasibility of evaluation, with newly diagnosed brain tumour patients. Study 1: Structured concept mapping techniques were undertaken with 30 health professionals, who identified strategies or items for improving care, and rated each of 42 items for importance, feasibility, and the extent to which such care was provided. Participants also provided data to interpret the relationship between items, which were translated into ‘maps’ of relationships between information and other aspects of health care using multidimensional scaling and hierarchical cluster analysis. Results were discussed by participants in small groups and individual interviews to understand the ratings, and facilitators and barriers to implementation. A care coordinator was rated as the most important strategy by health professionals. Two items directly related to information provision were also seen as highly important: "information to enable the patient or carer to ask questions" and "for doctors to encourage patients to ask questions". Qualitative analyses revealed that information provision was individualised, depending on patients’ information needs and preferences, demographic variables and distress, the characteristics of health professionals who provide information, the relationship between the individual patient and health professional, and influenced by the fragmented nature of the health care system. Based on quantitative and qualitative findings, a brain tumour specific question prompt list (QPL) was chosen for development and feasibility testing. A QPL consists of a list of questions that patients and carers may want to ask their doctors. It is designed to encourage the asking of questions in the medical consultation, allowing patients to control the content, and amount of information provided by health professionals. Study 2: The initial structure and content of the brain tumour specific QPL developed was based upon thematic analyses of 1) patient materials for brain tumour patients, 2) QPLs designed for other patient populations, and 3) clinical practice guidelines for the psychosocial care of glioma patients. An iterative process of review and refinement of content was undertaken via telephone interviews with a convenience sample of 18 patients and/or carers. Successive drafts of QPLs were sent to patients and carers and changes made until no new topics or suggestions arose in four successive interviews (saturation). Once QPL content was established, readability analyses and redrafting were conducted to achieve a sixth-grade reading level. The draft QPL was also reviewed by eight health professionals, and shortened and modified based on their feedback. Professional design of the QPL was conducted and sent to patients and carers for further review. The final QPL contained questions in seven colour-coded sections: 1) diagnosis; 2) prognosis; 3) symptoms and problems; 4) treatment; 5) support; 6) after treatment finishes; and 7) the health professional team. Study 3: A feasibility study was conducted to determine the acceptability of the QPL and the appropriateness of methods, to inform a potential future randomised trial to evaluate its effectiveness. A pre-test post-test design was used with a nonrandomised control group. The control group was provided with ‘standard information’, the intervention group with ‘standard information’ plus the QPL. The primary outcome measure was acceptability of the QPL to participants. Twenty patients from four hospitals were recruited a median of 1 month (range 0-46 months) after diagnosis, and 17 completed baseline and follow-up interviews. Six participants would have preferred to receive the information booklet (standard information or QPL) at a different time, most commonly at diagnosis. Seven participants reported on the acceptability of the QPL: all said that the QPL was helpful, and that it contained questions that were useful to them; six said it made it easier to ask questions. Compared with control group participants’ ratings of ‘standard information’, QPL group participants’ views of the QPL were more positive; the QPL had been read more times, was less likely to be reported as ‘overwhelming’ to read, and was more likely to prompt participants to ask questions of their health professionals. The results from the three studies of this research program add to the body of literature on information provision for brain tumour patients. Together, these studies suggest that a QPL may be appropriate for the neuro-oncology setting and acceptable to patients. The QPL aims to assist patients to express their information needs, enabling health professionals to better provide the type and amount of information that patients need to prepare for treatment and the future. This may help health professionals meet the challenge of giving patients sufficient information, without providing ‘too much’ or ‘unnecessary’ information, or taking away hope. Future studies with rigorous designs are now needed to determine the effectiveness of the QPL.

A recommendation method for online dating networks based on social relations and demographic information

A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.

A capabilities-based approach to obtaining a deeper understanding of information technology governance effectiveness : evidence from IT steering committees

Given the substantial investment in information technology (IT), and the significant impact IT has on organizational success, organizations consume considerable resources to manage acquisition and use of their IT resources. While various arguments proposed suggest which IT governance arrangements may work best, our understanding of the effectiveness of such initiatives is limited. We examine the relationship between the effectiveness of IT steering committee driven IT governance initiatives and firm's IT management and IT infrastructure related capabilities. We further propose that firm's ITrelated capabilities generated through IT governance initiatives should improve its business processes and firm-level performance. We test these relationships empirically by a field survey. Results suggest that firms' effectiveness of IT steering committee driven IT governance initiatives positively relates to the level of their IT-related capabilities. We also found positive relationships between IT-related capabilities and internal process-level performance. Our results also support that improvement in internal process-level performance positively relates to improvement in customer service and firm-level performance.

Semantics-based web service discovery using information retrieval techniques

This paper demonstrates an experimental study that examines the accuracy of various information retrieval techniques for Web service discovery. The main goal of this research is to evaluate algorithms for semantic web service discovery. The evaluation is comprehensively benchmarked using more than 1,700 real-world WSDL documents from INEX 2010 Web Service Discovery Track dataset. For automatic search, we successfully use Latent Semantic Analysis and BM25 to perform Web service discovery. Moreover, we provide linking analysis which automatically links possible atomic Web services to meet the complex requirements of users. Our fusion engine recommends a final result to users. Our experiments show that linking analysis can improve the overall performance of Web service discovery. We also find that keyword-based search can quickly return results but it has limitation of understanding users’ goals.

Minimizing information leakage of tree-based RFID authentication protocols using alternate tree-walking

The privacy of efficient tree-based RFID authentication protocols is heavily dependent on the branching factor on the top layer. Indefinitely increasing the branching factor, however, is not a viable option. This paper proposes the alternate-tree walking scheme as well as two protocols to circumvent this problem. The privacy of the resulting protocols is shown to be comparable to that of linear-time protocols, where there is no leakage of information, whilst reducing the computational load of the database by one-third of what is required of tree-based protocols during authentication. We also identify and address a limitation in quantifying privacy in RFID protocols.

Towards a Deeper Understanding of Information Technology Governance Effectiveness : A Capabilities-Based Approach

We examine the relationship between the effectiveness of IT steering committee-driven IT governance initiatives and firm’s IT management and IT infrastructure related capabilities. We test these relationships empirically by a field survey of 216 firms. Results of this study suggest that a firms’ effectiveness of IT steering committee-driven IT governance initiatives positively relate to the level of their IT-related capabilities. We also found positive relationships between IT-related capabilities and internal process-level performance, which positively relate to improvement in customer service and firm-level performance. For researchers, we demonstrate that the resourcebased theory provides a more robust explanation of the determinants of firms IT governance initiatives. This would be ideal in evaluating other IT governance initiatives effectiveness in relation to how they contribute to building performance-differentiating IT-related capabilities. For decision makers, we hope our study has reiterated the notion that IT governance is truly a coordinated effort, embracing all levels of human resources.

Development of a data mining-based analysis framework for multi-attribute construction project information

Data mining techniques extract repeated and useful patterns from a large data set that in turn are utilized to predict the outcome of future events. The main purpose of the research presented in this paper is to investigate data mining strategies and develop an efficient framework for multi-attribute project information analysis to predict the performance of construction projects. The research team first reviewed existing data mining algorithms, applied them to systematically analyze a large project data set collected by the survey, and finally proposed a data-mining-based decision support framework for project performance prediction. To evaluate the potential of the framework, a case study was conducted using data collected from 139 capital projects and analyzed the relationship between use of information technology and project cost performance. The study results showed that the proposed framework has potential to promote fast, easy to use, interpretable, and accurate project data analysis.

Graph-based concept weighting for medical information retrieval

This paper presents a graph-based method to weight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations between concepts. This representation naturally captures dependencies between concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations. We apply existing graph-based term weighting methods to weight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as well as encapsulates terms belonging to a single medical entity into a single concept. In addition, we further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain. Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches.

Ontology-based feature modeling for construction information extraction from a building information model

A building information model (BIM) provides a rich representation of a building's design. However, there are many challenges in getting construction-specific information from a BIM, limiting the usability of BIM for construction and other downstream processes. This paper describes a novel approach that utilizes ontology-based feature modeling, automatic feature extraction based on ifcXML, and query processing to extract information relevant to construction practitioners from a given BIM. The feature ontology generically represents construction-specific information that is useful for a broad range of construction management functions. The software prototype uses the ontology to transform the designer-focused BIM into a construction-specific feature-based model (FBM). The formal query methods operate on the FBM to further help construction users to quickly extract the necessary information from a BIM. Our tests demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.

Pattern-based topic models for information filtering

Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.

Pattern-based topics for document modelling in information filtering

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is proposed. The main distinctive features of the proposed model include: (1) user information needs are generated in terms of multiple topics; (2) each topic is represented by patterns; (3) patterns are generated from topic models and are organized in terms of their statistical and taxonomic features, and; (4) the most discriminative and representative patterns, called Maximum Matched Patterns, are proposed to estimate the document relevance to the user’s information needs in order to filter out irrelevant documents. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models