Predicting Text Quality: Metrics for Content, Organization and Reader Interest

When people read articles---news, fiction or technical---most of the time if not always, they form perceptions about its quality. Some articles are well-written and others are poorly written. This thesis explores if such judgements can be automated so that they can be incorporated into applications such as information retrieval and automatic summarization. Text quality does not involve a single aspect but is a combination of numerous and diverse criteria including spelling, grammar, organization, informative nature, creative and beautiful language use, and page layout. In the education domain, comprehensive lists of such properties are outlined in the rubrics used for assessing writing. But computational methods for text quality have addressed only a handful of these aspects, mainly related to spelling, grammar and organization. In addition, some text quality aspects could be more relevant for one genre versus another. But previous work have placed little focus on specialized metrics based on the genre of texts. This thesis proposes new insights and techniques to address the above issues. We introduce metrics that score varied dimensions of quality such as content, organization and reader interest. For content, we present two measures: specificity and verbosity level. Specificity measures the amount of detail present in a text while verbosity captures which details are essential to include. We measure organization quality by quantifying the regularity of the intentional structure in the article and also using the specificity levels of adjacent sentences in the text. Our reader interest metrics aim to identify engaging and interesting articles. The development of these measures is backed by the use of articles from three different genres: academic writing, science journalism and automatically generated summaries. Proper presentation of content is critical during summarization because summaries have a word limit. Our specificity and verbosity metrics are developed with this genre as the focus. The argumentation structure of academic writing lends support to the idea of using intentional structure to model organization quality. Science journalism articles convey research findings in an engaging manner and are ideally suited for the development and evaluation of measures related to reader interest

Proceedings of the 10th international conference on disability, virtual reality and associated technologies (ICDVRAT 2014)

The proceedings of the conferenc

Economic evaluations for health technologies with an evolving evidence base: a case study of transcatheter aortic valve implantation

The primary aim of this thesis is to investigate the challenges in conducting economic evaluations for medical devices with evolving evidence bases. While economic evaluations for capital projects and medicines are well established in theory and practice, the same cannot be said for novel medical devices. New medical devices are often expensive and rely on scarce evidence for efficacy and cost. This increases uncertainty surrounding their clinical and cost effectiveness. In addition, as fewer formal procedures exist for evaluating devices relative to medicines, evidence bases are weak and health technology assessment agencies are reluctant to make rapid decisions. To address these issues a continuous iterative framework developed and proposed for economic evaluations of medical devices. In this thesis, using Transcatheter Aortic Valve Implantation (TAVI) as a case study, an iterative economic evaluation, employing Bayesian techniques, is developed to investigate how the challenges associated with medical devices can be overcome to produce an efficient and informative economic evaluation. This study is the first to investigate these challenges and identify solutions while conducting an economic evaluation early in a device’s life cycle, using the proposed continuous iterative framework. The consideration of Access with Evidence Development schemes to overcome these challenges and balance access with evidence generation for expensive and novel medical devices, with evolving evidence, is another important contribution of the thesis. Transcatheter Aortic Valve Implantation (TAVI) is a novel treatment for severe Aortic Stenosis for operable and inoperable patients. The iterative economic evaluation concludes that TAVI can be considered cost effective for inoperable patients compared to medical management. There is little value in commissioning new research for continued data collection for this group. However, the continued collection of evidence via the UK TAVI registry as indicated in the National Institute of Clinical Excellence (NICE) guidelines will ensure up to date evidence is available to inform future decisions regarding TAVI in this patient group. For operable patients, the iterative model could not conclude that TAVI was cost effective compared to Aortic Valve Replacement (AVR). However, additional evidence of improved outcomes from TAVI should enhance its cost effectiveness for these patients. The Bayesian value of information analysis indicates that further information on short and long term probability, resource and quality of life parameters is most valuable and the optimal research design for collecting such information is a registry