Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Assessing Crash Risks on Curves

In Queensland, curve related crashes contributed to 63.44% of fatalities, and 25.17% required hospitalisation. In addition, 51.1% of run-off-road crashes occurred on obscured or open-view road curves (Queensland Transport, 2006). This paper presents a conceptual framework for an in-vehicle system, which assesses crash risk when a driver is manoeuvring on a curve. Our approach consists of using Intelligent Transport Systems (ITS) to collect information about the driving context. The driving context corresponds to information about the environment, driver, and vehicle gathered from sensor technology. Sensors are useful to detect drivers’ high-risk situations such as curves, fogs, drivers’ fatigue or slippery roads. However, sensors can be unreliable, and therefore the information gathered from them can be incomplete or inaccurate. In order to improve the accuracy, a system is built to perform information fusion from past and current driving information. The integrated information is analysed using ubiquitous data mining techniques and the results are later used in a Coupled Hidden Markov Model (CHMM), to learn and classify the information into different risk categories. CHMM is used to predict the probability of crash on curves. Based on the risk assessment, our system provides appropriate intervention to the driver. This approach could allow the driver to have sufficient time to react promptly. Hence, this could potentially promote safe driving and decrease curve related injuries and fatalities

An Intelligent Data Mining System to Detect Health Care Fraud

The chapter begins with an overview of the types of healthcare fraud. Next, there is a brief discussion of issues with the current fraud detection approaches. The chapter then develops information technology based approaches and illustrates how these technologies can improve current practice. Finally, there is a summary of the major findings and the implications for healthcare practice

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

Placing the Czech shadow banking sector under the light

The size of the shadow banking sector (SBS) has more than doubled in the Czech Republic over the last decade. This places a potential burden on policy makers. On the one hand, the SBS complements regular banking by expanding access to credit and investments, enabling better risk sharing and maturity transformation, and supporting market liquidity. On the other hand, SBS activities can put the stability of the financial system at risk and amplify its procyclicality by exacerbating the buildup of leverage and asset price bubbles. We implement a FAVAR model of the Czech economy to determine the impact of macroeconomic factors on the SBS. We find that the SBS: (i) is sensitive to changes in market interest rates and term spread; (ii) exhibits great procyclicality; (iii) can act as a complement to regular banking and satisfy some additional demand for credit. We also define some potential risks of continued growth of the SBS, linked to our empirical evidence.Web of Science29128