What do patients want to know? An empirical approach to explanation generation and validation

This article describes an empirical, user-centred approach to explanation design. It reports three studies that investigate what patients want to know when they have been prescribed medication. The question is asked in the context of the development of a drug prescription system called OPADE. The system is aimed primarily at improving the prescribing behaviour of physicians, but will also produce written explanations for indirect users such as patients. In the first study, a large number of people were presented with a scenario about a visit to the doctor, and were asked to list the questions that they would like to ask the doctor about the prescription. On the basis of the results of the study, a categorization of question types was developed in terms of how frequently particular questions were asked. In the second and third studies a number of different explanations were generated in accordance with this categorization, and a new sample of people were presented with another scenario and were asked to rate the explanations on a number of dimensions. The results showed significant differences between the different explanations. People preferred explanations that included items corresponding to frequently asked questions in study 1. For an explanation to be considered useful, it had to include information about side effects, what the medication does, and any lifestyle changes involved. The implications of the results of the three studies are discussed in terms of the development of OPADE's explanation facility.

Past, present and future mathematical models for buildings (ii)

This article is the second part of a review of the historical evolution of mathematical models applied in the development of building technology. The first part described the current state of the art and contrasted various models with regard to the applications to conventional buildings and intelligent buildings. It concluded that mathematical techniques adopted in neural networks, expert systems, fuzzy logic and genetic models, that can be used to address model uncertainty, are well suited for modelling intelligent buildings. Despite the progress, the possible future development of intelligent buildings based on the current trends implies some potential limitations of these models. This paper attempts to uncover the fundamental limitations inherent in these models and provides some insights into future modelling directions, with special focus on the techniques of semiotics and chaos. Finally, by demonstrating an example of an intelligent building system with the mathematical models that have been developed for such a system, this review addresses the influences of mathematical models as a potential aid in developing intelligent buildings and perhaps even more advanced buildings for the future.

Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

The possibility of using a radial basis function neural network (RBFNN) to accurately recognise and predict the onset of Parkinson’s disease tremors in human subjects is discussed in this paper. The data for training the RBFNN are obtained by means of deep brain electrodes implanted in a Parkinson disease patient’s brain. The effectiveness of a RBFNN is initially demonstrated by a real case study.

Artificial keys for botanical identification using a multilayer perceptron neural network (MLP)

In this paper, practical generation of identification keys for biological taxa using a multilayer perceptron neural network is described. Unlike conventional expert systems, this method does not require an expert for key generation, but is merely based on recordings of observed character states. Like a human taxonomist, its judgement is based on experience, and it is therefore capable of generalized identification of taxa. An initial study involving identification of three species of Iris with greater than 90% confidence is presented here. In addition, the horticulturally significant genus Lithops (Aizoaceae/Mesembryanthemaceae), popular with enthusiasts of succulent plants, is used as a more practical example, because of the difficulty of generation of a conventional key to species, and the existence of a relatively recent monograph. It is demonstrated that such an Artificial Neural Network Key (ANNKEY) can identify more than half (52.9%) of the species in this genus, after training with representative data, even though data for one character is completely missing.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Professionalism in digitally-mediated project work

Fieldwork in a major construction programme is used to examine what is meant by professionalism where large integrated digital systems are used to design, deliver, and maintain buildings and infrastructure. The increasing ‘professionalization’ of the client is found to change other professional roles and interactions in project delivery. New technologies for approvals and workflow monitoring are associated with new occupational groups; new kinds of professional accountability; and a greater integration across professional roles. Further conflicts also arise, where occupational groups have different understandings of project deliverables and how they are competently achieved. The preliminary findings are important for an increasing policy focus on shareable data, in order for building owners and operators to improve the cost, value, handover and operation of complex buildings. However, it will also have an impact on wider public decision-making processes, professional autonomy, expertise and interdependence. These findings are considered in relation to extant literatures, which problematize the idea of professionalism; and the shift from drawings to shareable data as deliverables. The implications for ethics in established professions and other occupational groups are discussed; directions are suggested for further scholarship on professionalism in digitally mediated project work to improve practices which will better serve society.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.