Determining clinical pharmacy workload by patient disease classification in medical and surgical patients

Aim: To determine the time needed to provide clinical pharmacy services to individual patient episodes for medical and surgical patients and the effect of patient presentation and complexity on the clinical pharmacy workload. Method: During a 5-month period in 2006 at two general hospitals, pharmacists recorded a defined range of activities that they provided for patients, including the actual times required for these tasks. A customised database linked to the two hospitals' patient administration systems stored the data according to the specific patient episode number. The influence of patient presentation and complexity on the clinical pharmacy activities provided was also examined. Results: The average time required by pharmacists to undertake a medication history interview and medication reconciliation was 9.6 (SD 4.9) minutes. Interventions required 5.7 (SD 4.6) minutes, clinical review of the medical record 5.5 (SD 4.0) minutes and medication order review 3.5 (SD 2.0) minutes. For all of these activities, the time required for medical patients was greater than for surgical patients and greater for 'complicated' patients. The average time required to perform all clinical pharmacy activities for 1071 completed patient episodes was 14.4 (SD 10.9) minutes and was greater for medical and 'complicated' patients. Conclusion: The time needed to provide clinical pharmacy services was affected by whether the patients were medical or surgical. The existence of comorbidities or complications affected these times. The times required to perform clinical pharmacy activities may not be consistent with recently proposed staff ratios for the provision of a basic clinical pharmacy service.

An initial classification of branding strategy in Australian arts organisations

This research examined the corporate branding approaches and strategies adopted by six prominent Australian arts and cultural organisations. The aim of this exploration was to identify patterns in branding across different arts and cultural organisations, and attempt to provide an initial classification for understanding how these organisations approach branding strategy. We found that three factors influenced branding strategy in the surveyed organisations, viz., the focus of branding process, the degree of consistency in branding communication, and the required level of customers’ involvement in the branded products. The organisations studied were then plotted on a continuum that considered each of these factors.

A classification for content adaptation system

Content adaptation is an attractive solution for the ever growing desktop based Web content delivered to the user via heterogeneous devices, in order to provide acceptable experience while surfing the Web. Bridging the mismatch between the rich content and the user device's resources (display, processing, navigation, network bandwidth, media support) without user intervention requires a proactive behavior. While content adaptation poses multitude of benefits, without proper strategies, adaptation will not be truly optimized. There have been many projects focused on content adaptation that have been designed with different goals and approaches. In this paper, we introduce a comprehensive classification for content adaptation system. The classification is used to group the approaches applied in the implementation of existing content adaptation system. Survey on some content adaptation systems also been provided. We also present the research spectrum in content adaptation and discuss the challenges.

Using Choquet integrals for kNN approximation and classification

k-nearest neighbors (kNN) is a popular method for function approximation and classification. One drawback of this method is that the nearest neighbors can be all located on one side of the point in question x. An alternative natural neighbors method is expensive for more than three variables. In this paper we propose the use of the discrete Choquet integral for combining the values of the nearest neighbors so that redundant information is canceled out. We design a fuzzy measure based on location of the nearest neighbors, which favors neighbors located all around x.

Lung nodules detection by ensemble classification

A method is presented that achieves lung nodule detection by classification of nodule and non-nodule patterns. It is based on random forests which are ensemble learners that grow classification trees. Each tree produces a classification decision, and an integrated output is calculated. The performance of the developed method is compared against that of the support vector machine and the decision tree methods. Three experiments are performed using lung scans of 32 patients including thousands of images within which nodule locations are marked by expert radiologists. The classification errors and execution times are presented and discussed. The lowest classification error (2.4%) has been produced by the developed method.

A case study on classification reliability

The reliability of an induced classifier can be affected by several factors including the data oriented factors and the algorithm oriented factors. In some cases, the reliability could also be affected by knowledge oriented factors. In this paper, we analyze three special cases to examine the reliability of the discovered knowledge. Our case study results show that (1) in the cases of mining from low quality data, rough classification approach is more reliable than exact approach which in general tolerate to low quality data; (2) Without sufficient large size of the data, the reliability of the discovered knowledge will be decreased accordingly; (3) The reliability of point learning approach could easily be misled by noisy data. It will in most cases generate an unreliable interval and thus affect the reliability of the discovered knowledge. It is also reveals that the inexact field is a good learning strategy that could model the potentials and to improve the discovery reliability.

An illustration of variable precision rough set theory : The gender classification of the European barn swallow (Hirundo rustica)

This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a ‘leave n out’ approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the ‘leave n out’ approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.

Object classification under ignorance using CaRBS : the case of the European barn swallow

This article investigates the potential of a novel technique for object classification, called Classification and Ranking Belief Simplex (CaRBS), which is based on the Dempster-Shafer theory of evidence. As such, the classification of objects and the evidence from their characteristics have a level of ignorance associated with them. Its potential is exposited in the application of the classification of European barn swallows according to their gender. The classification of biological data in the presence of ignorance about such data sets is a common problem in biology. Comparisons of the results from CaRBS with those from multivariate discriminant analysis and neural networks are made. Also shown throughout the investigation is the interpretability of the results with the utilisation of the simplex plot method of representing data

Signalling corporate collapse using a dual classification scheme:Australian evidence

Regardless of the technical procedure used in signalling corporate collapse, the bottom line rests on the predictive power of the corresponding statistical model. In that regard, it is imperative to empirically test the model using a data sample of both collapsed and non-collapsed companies. A superior model is one that successfully classifies collapsed and non-collapsed companies in their respective categories with a high degree of accuracy. Empirical studies of this nature have thus far done one of two things. (1) Some have classified companies based on a specific statistical modelling process. (2) Some have classified companies based on two (sometimes – but rarely – more than two) independent statistical modelling processes for the purposes of comparing one with the other. In the latter case, the mindset of the researchers has been – invariably – to pitch one procedure against the other. This paper raises the question, why pitch one statistical process against another; why not make the two procedures work together? As such, this paper puts forward an innovative dual-classification scheme for signalling corporate collapse: dual in the sense that it relies on two statistical procedures concurrently. Using a data sample of Australian publicly listed companies, the proposed scheme is tested against the traditional approach taken thus far in the pertinent literature. The results demonstrate that the proposed dual-classification scheme signals collapse with a higher degree of accuracy.

Hybrid classification of pulmonary nodules

Automated classification of lung nodules is challenging because of the variation in shape and size of lung nodules, as well as their associated differences in their images. Ensemble based learners have demonstrated the potentialof good performance. Random forests are employed for pulmonary nodule classification where each tree in the forest produces a classification decision, and an integrated output is calculated. A classification aided by clustering approach is proposed to improve the lung nodule classification performance. Three experiments are performed using the LIDC lung image database of 32 cases. The classification performance and execution times are presented and discussed.

Multi-classifier classification of spam email on an ubiquitous multi-core architecture

This paper presents an innovative fusion based multi-classifier email classification on a ubiquitous multi-core architecture. Many approaches use text-based single classifiers or multiple weakly trained classifiers to identify spam messages from a large email corpus. We build upon our previous work on multi-core by apply our ubiquitous multi-core framework to run our fusion based multi-classifier architecture. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our proposed multi-classifier based filtering system. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at the average cost of 1.4 ms. We also reduced the instance of false positive, which is one of the key challenges in spam filtering system, and increases email classification accuracy substantially compared with single classification techniques.

Email categorization using (2+1)-tier classification algorithms

In this paper we have proposed a spam filtering technique using (2+1)-tier classification approach. The main focus of this paper is to reduce the false positive (FP) rate which is considered as an important research issue in spam filtering. In our approach, firstly the email message will classify using first two tier classifiers and the outputs will appear to the analyzer. The analyzer will check the labeling of the output emails and send to the corresponding mailboxes based on labeling, for the case of identical prediction. If there are any misclassifications occurred by first two tier classifiers then tier-3 classifier will invoked by the analyzer and the tier-3 will take final decision. This technique reduced the analyzing complexity of our previous work. It has also been shown that the proposed technique gives better performance in terms of reducing false positive as well as better accuracy.

Identification and classification of animal fibres using artificial neural networks

It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.