Hierarchical case-based reasoning to support knitwear design

Knitwear design is a creative activity that is hard to automate using the computer. The production of the associated knitting pattern, however, is repetitive, time-consuming and error-prone, calling for automation. Our objectives are two-fold: to facilitate the design and to ease the burden of calculations and checks in pattern production. We conduct a feasibility study for applying case-based reasoning in knitwear design: we describe appropriate methods and show their application.

Discrimination of healthy and cancer cells of the bladder by metabolic state, based on autofluorescence

Bladder cancer is among the most common cancers worldwide (4th in men). It is responsible for high patient morbidity and displays rapid recurrence and progression. Lack of sensitivity of gold standard techniques (white light cystoscopy, voided urine cytology) means many early treatable cases are missed. The result is a large number of advanced cases of bladder cancer which require extensive treatment and monitoring. For this reason, bladder cancer is the single most expensive cancer to treat on a per patient basis. In recent years, autofluorescence spectroscopy has begun to shed light into disease research. Of particular interest in cancer research are the fluorescent metabolic cofactors NADH and FAD. Early in tumour development, cancer cells often undergo a metabolic shift (the Warburg effect) resulting in increased NADH. The ratio of NADH to FAD ("redox ratio") can therefore be used as an indicator of the metabolic status of cells. Redox ratio measurements have been used to differentiate between healthy and cancer breast cells and to monitor cellular responses to therapies. Here, we have demonstrated, using healthy and bladder cancer cell lines, a statistically significant difference in the redox ratio of bladder cancer cells, indicative of a metabolic shift. To do this we customised a standard flow cytometer to excite and record fluorescence specifically from NADH and FAD, along with a method for automatically calculating the redox ratio of individual cells within large populations. These results could inform the design of novel probes and screening systems for the early detection of bladder cancer.

Supporting knitwear design using case-based reasoning

Knitwear design is a creative activity that is hard to automate using the computer. The production of the associated knitting pattern, however, is repetitive, time-consuming and error-prone, calling for automation. Our objectives are two-fold: To facilitate the design and to ease the burden of calculations and checks in pattern production. We conduct a feasibility study for applying case-based reasoning in knitwear design: We describe appropriate methods and show how they can be implemented. © Cranfield University 2009.

Case-based reasoning enabling database mining for cryo-preserving algae applications

Case-based Reasoning's (CBR) origins were stimulated by a desire to understand how people remember information and are in turn reminded of information, and that subsequently it was recognized that people commonly solve problems by remembering how they solved similar problems in the past. Thus CBR became an appropriate way to find out the most suitable solution method for a new problem based on the old methods for the same or even similar problems. The research highlights how to use CBR to aid biologists in finding the best method to cryo preserve algae. The study found CBR could be used successfully to find the similarity percentage between the new algae and old cases in the case base. The prediction result showed approximately 93.75% accuracy, which proves the CBR system can offer appropriate recommendations for most situations. © 2011 IEEE.

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

Case based design of knitwear

In the developed world we are surrounded by man-made objects, but most people give little thought to the complex processes needed for their design. The design of hand knitting is complex because much of the domain knowledge is tacit. The objective of this thesis is to devise a methodology to help designers to work within design constraints, whilst facilitating creativity. A hybrid solution including computer aided design (CAD) and case based reasoning (CBR) is proposed. The CAD system creates designs using domain-specific rules and these designs are employed for initial seeding of the case base and the management of constraints. CBR reuses the designer's previous experience. The key aspects in the CBR system are measuring the similarity of cases and adapting past solutions to the current problem. Similarity is measured by asking the user to rank the importance of features; the ranks are then used to calculate weights for an algorithm which compares the specifications of designs. A novel adaptation operator called rule difference replay (RDR) is created. When the specifications to a new design is presented, the CAD program uses it to construct a design constituting an approximate solution. The most similar design from the case-base is then retrieved and RDR replays the changes previously made to the retrieved design on the new solution. A measure of solution similarity that can validate subjective success scores is created. Specification similarity can be used as a guide whether to invoke CBR, in a hybrid CAD-CBR system. If the newly resulted design is suffciently similar to a previous design, then CBR is invoked; otherwise CAD is used. The application of RDR to knitwear design has demonstrated the flexibility to overcome deficiencies in rules that try to automate creativity, and has the potential to be applied to other domains such as interior design.

Mobile case-based decision support for intelligent patient knowledge management

Hospitals everywhere are integrating health data using electronic health record (EHR) systems, and disparate and multimedia patient data can be input by different caregivers at different locations as encapsulated patient profiles. Healthcare institutions are also using the flexibility and speed of wireless computing to improve quality and reduce costs. We are developing a mobile application that allows doctors to efficiently record and access complete and accurate real-time patient information. The system integrates medical imagery with textual patient profiles as well as expert interactions by healthcare personnel using knowledge management and case-based reasoning techniques. The application can assist other caregivers in searching large repositories of previous patient cases. Patients' symptoms can be input to a portable device and the application can quickly retrieve similar profiles which can be used to support effective diagnoses and prognoses by comparing symptoms, treatments, diagnosis, test results and other patient information. © 2007 Sage Publications.

Asthma management : implementation of short-acting beta agonist guidelines in Western Australia:a unique collaboration

Background: The Respiratory Health Network in Western Australia developed the Asthma Model of Care in 2010 which incorporates best practice guidelines. At the same time short-acting beta agonist guidelines (SABA) were developed by stakeholder consensus at University of Western Australia (UWA) and incorporated the use of an Asthma Action Plan Card. Objective: To report on the implementation of a key component of the WA Asthma Model of Care, the SABA guidelines that incorporate the Asthma Action Plan card. Methods: Implementation strategies included lectures, direct pharmacy detailing, media releases, and information packs (postal and electronic). Groups targeted included pharmacists, consumers and medical practitioners. Results: State-based (n=18) and national (n=6) professional organisations were informed about the launch of the guidelines into practice in WA. In the four-month implementation period more than 47,000 Asthma Action Plan Cards were distributed, primarily to community pharmacies. More than 500 pharmacies were provided with information packs or individual detailing. More than 10,000 consumers were provided with information about the guidelines. Conclusions and implications: The collaboration of stakeholders in this project allowed for widespread access to various portals which, in turn, resulted in a multifaceted approach in disseminating information. Ongoing maintenance programs are required to sustain and build on the momentum of the implementation program and to ultimately address patient outcomes and practice change, which would be the longer-term goals of such a project. Future research will seek to ascertain the impact of the card on patient outcomes in WA.

Community accessibility of health information and the consequent impact for translation into community languages

This paper reports on a research project that investigated the accessibility of health information and the consequent impact for translation into community languages. This is a critical aspect of the mediation of intercultural and interlingual communication in the domain of public health information and yet very little research has been undertaken to address such issues. The project was carried out in collaboration with the New South Wales Multicultural Health Communication Service (MHCS), which provides advice and services to state-based health professionals aiming to communicate with non-English speaking communities. The research employed a mixed-method and action research based approach involving two phases. The primary focus of this paper is to discuss major quantitative findings from the first pilot phase, which indicated that there is much room to improve the way in which health information is written in English for effective community-wide communication within a multilingual society.

Imagery in multi-modal object learning

Spatial objects may not only be perceived visually but also by touch. We report recent experiments investigating to what extent prior object knowledge acquired in either the haptic or visual sensory modality transfers to a subsequent visual learning task. Results indicate that even mental object representations learnt in one sensory modality may attain a multi-modal quality. These findings seem incompatible with picture-based reasoning schemas but leave open the possibility of modality-specific reasoning mechanisms.

Decision support based on genomics:integration of data-and knowledge-driven reasoning

The breadth and depth of available clinico-genomic information, present an enormous opportunity for improving our ability to study disease mechanisms and meet the individualised medicine needs. A difficulty occurs when the results are to be transferred 'from bench to bedside'. Diversity of methods is one of the causes, but the most critical one relates to our inability to share and jointly exploit data and tools. This paper presents a perspective on current state-of-the-art in the analysis of clinico-genomic data and its relevance to medical decision support. It is an attempt to investigate the issues related to data and knowledge integration. Copyright © 2010 Inderscience Enterprises Ltd.

The views of corporate managers on the current state of, and future prospects for, social reporting in Bangladesh: an engagement based study

Purpose – This paper seeks to respond to recent calls for more engagement-based studies of corporate social reporting (CSR) practice by examining the views of corporate managers on the current state of, and future prospects for, social reporting in Bangladesh. Design/methodology/approach – The paper uses a series of interviews with senior managers from 23 Bangladeshi companies representing the multinational, domestic private and public sectors. Findings – Key findings are that the main motivation behind current reporting practice lies in a desire on the part of corporate management to manage powerful stakeholder groups, whilst perceived pressure from external forces, notably parent companies' instructions and demands from international buyers, is driving the process forward. In the latter context it appears that adoption of international social accounting standards and codes is likely to become more prevalent in the future. Reservations are expressed as to whether such a passive compliance strategy is likely to achieve much in the way of real changes in corporate behaviour, particularly when Western developed standards and codes are imposed without consideration of local cultural, economic and social factors. Indeed, such imposition could be regarded as little more than an example of the erection of non-tariff trade barriers rather than representing any meaningful move towards empowering indigenous stakeholder groups. Originality/value – The paper contributes to the literature on CSR in developing countries where there is a distinct lack of engagement-based published studies.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.