The LH5 model for data mining

In the age of E-Business many companies faced with massive data sets that must be analysed for gaining a competitive edge. these data sets are in many instances incomplete and quite often not of very high quality. Although statistical analysis can be used to pre-process these data sets, this technique has its own limitations. In this paper we are presenting a system - and its underlying model - that can be used to test the integrity of existing data and pre-process the data into clearer data sets to be mined. LH5 is a rule-based system, capable of self-learning and is illustrated using a medical data set.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Reflections of professional practice: using electronic discourse analysis networks (EDANs) to examine embedded business emails

Emails have become a central genre in business communication, reflecting both how people communicate and how they go about their professional practices. This chapter examines embedded business emails as reflections of the professional practices of the regulatory and policy department of a multinational based in London, UK. It argues that the nature of online communication in international organisations, with its high levels of intertextuality and interdiscursivity, requires multidimensional analytical approaches that are capable of capturing its complexity and dynamics. To this end, the chapter introduces electronic discourse analysis networks (EDANs) as one example of such approaches. It begins with a brief review of the literature that has informed the study reported on here before it discusses EDANs as its analytical framework. Using a group of embedded emails and a number of networked data sets, the chapter shows how EDANs can be used to further our understanding of professional online communication.

"One of our hosts in another country": Challenges of data geolocation in cloud storage

Physical location of data in cloud storage is an increasingly urgent problem. In a short time, it has evolved from the concern of a few regulated businesses to an important consideration for many cloud storage users. One of the characteristics of cloud storage is fluid transfer of data both within and among the data centres of a cloud provider. However, this has weakened the guarantees with respect to control over data replicas, protection of data in transit and physical location of data. This paper addresses the lack of reliable solutions for data placement control in cloud storage systems. We analyse the currently available solutions and identify their shortcomings. Furthermore, we describe a high-level architecture for a trusted, geolocation-based mechanism for data placement control in distributed cloud storage systems, which are the basis of an on-going work to define the detailed protocol and a prototype of such a solution. This mechanism aims to provide granular control over the capabilities of tenants to access data placed on geographically dispersed storage units comprising the cloud storage.

Barriers to working with people affected by cancer: Preliminary qualitative data from a mixed methods exploration of practitioner perspectives in nutritional therapy

Introduction: Cancer is a leading cause of death worldwide. Nutrition may affect occurrence, recurrence and survival rates and many cancer patients and survivors seek individualized nutrition advice. Appropriately skilled nutritional therapy (NT) practitioners may be well-placed to safely provide this advice, but little is known of their perspectives on working with people affected by cancer. This mixed-methods study seeks to explore their views on training, barriers to practice, use of evidence, and other resources, to support the development of safe evidence-based practice. Preliminary data on barriers to practice are reported here. Methods: Two cohorts of NT practitioners were recruited from all UK registered NT practitioners, by an on-line anonymous survey. 84 cancer practitioners (CP) and 165 non-cancer practitioners (NCP) were recruited. Mixed quantitative and qualitative data was collected by the survey. Content analysis was used to analyze qualitative data on the use of evidence, barriers to practice and perceived needs for working with clients with cancer, for further exploration using interviews and focus groups. Preliminary results: For the NCP cohort, exploring themes of perceived barriers to working with people affected by cancer suggested that perceived complexity, risk and need for caution in this area of practice were important barriers. Insufficient specialist knowledge and skills also emerged as barriers. Some NCPs perceived opposition from medical practitioners and other mainstream healthcare professions as an obstacle to starting cancer practice. To overcome these barriers, specialist training emerged as most important. For the CP cohort, in exploring the skills they considered enabled them to undertake cancer work, specialist clinical and technical knowledge emerged strongly. Only 10% CP participants did not want more work with people affected by cancer. 10% CPs reported some NHS referrals, whereas most received clients by self-referral or from other practitioners. When considering barriers that impede their cancer practice, the dominant categories for CPs were hostility or opposition by mainstream oncology professionals, and lack of dialogue and engagement with them. To overcome these barriers, CPs desired engagement with oncology professionals and recognized specialist cancer NT training. For both NCPs and CPs, evidence resources, practice guidelines and practitioner support networks also emerged as potential enablers to cancer practice. Conclusions: This is the first detailed exploration of NT practitioners’ perceived barriers to working with people affected by cancer. Acquiring specialist skills and knowledge appears important to enable NCPs to start cancer work, and for CPs with these skills, the perceived barriers appear foremost in the relationship with mainstream cancer professionals. Further exploration of these themes, and other NT practitioner perspectives on working with people affected by cancer, is underway. This work will inform and support the development of professional practice, training and other resources.