Tackling Acceptability Issues in Communities of Practice by Providing a Lightweight Email-based Interface to eLogbook: a Web 2.0 Collaborative Activity and Asset Management System

eLogbook is a Web-based collaborative environment designed for communities of practice. It enables users to manage joint activities, share related assets and get contextual awareness. In addition to the original Web- based access, an email-based eLogbook interface is under development. The purpose of this lightweight interface is twofold. First, it eases eLogbook access when using smart phones or PDA. Second, it eases eLogbook acceptance for community members hesitating to learn an additional Web environment. Thanks to the proposed interface, members of a community can benefit from the ease of use of an email client combined with the power of an activity and asset management system without burden. The Web-based eLogbook access can be kept for supporting further community evolutions, when participation becomes more regular and activities become more complex. This paper presents the motivation, the design and the incentives of the email-based eLogbook interface

Pharmacovigilance Decision Support : The value of Disproportionality Analysis Signal Detection Methods, the development and testing of Covariability Techniques, and the importance of Ontology

The cost of adverse drug reactions to society in the form of deaths, chronic illness, foetal malformation, and many other effects is quite significant. For example, in the United States of America, adverse reactions to prescribed drugs is around the fourth leading cause of death. The reporting of adverse drug reactions is spontaneous and voluntary in Australia. Many methods that have been used for the analysis of adverse drug reaction data, mostly using a statistical approach as a basis for clinical analysis in drug safety surveillance decision support. This thesis examines new approaches that may be used in the analysis of drug safety data. These methods differ significantly from the statistical methods in that they utilize co variability methods of association to define drug-reaction relationships. Co variability algorithms were developed in collaboration with Musa Mammadov to discover drugs associated with adverse reactions and possible drug-drug interactions. This method uses the system organ class (SOC) classification in the Australian Adverse Drug Reaction Advisory Committee (ADRAC) data to stratify reactions. The text categorization algorithm BoosTexter was found to work with the same drug safety data and its performance and modus operandi was compared to our algorithms. These alternative methods were compared to a standard disproportionality analysis methods for signal detection in drug safety data including the Bayesean mulit-item gamma Poisson shrinker (MGPS), which was found to have a problem with similar reaction terms in a report and innocent by-stander drugs. A classification of drug terms was made using the anatomical-therapeutic-chemical classification (ATC) codes. This reduced the number of drug variables from 5081 drug terms to 14 main drug classes. The ATC classification is structured into a hierarchy of five levels. Exploitation of the ATC hierarchy allows the drug safety data to be stratified in such a way as to make them accessible to powerful existing tools. A data mining method that uses association rules, which groups them on the basis of content, was used as a basis for applying the ATC and SOC ontologies to ADRAC data. This allows different views of these associations (even very rare ones). A signal detection method was developed using these association rules, which also incorporates critical reaction terms.Doctor of Philosoph

Email classification for automated service handling

We describe the experience and lessons learned from developing a range of electronic services for a specialist engineering company. We are using a custom workflow management system as the base for a range of services which are offered via a multi-modal portal, using a language-based approach to extracting information from HTML forms, email, and SMS. We describe the email classification experiments we have carried out and discuss the development of customer services based on automatic email classification

Email classification for automated service handling

We describe the experience and lessons learned from developing a range of electronic services for a specialist engineering company. We are using a custom workflow management system as the base for a range of services which are offered via a multimodal portal, using a language-based approach to extracting information from HTML forms, email, and SMS. We describe the email classification experiments we have carried out and discuss the development of customer services based on automatic email classification