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Classification Tool (classification + tool)
Selected AbstractsAssessing patient category/dependence systems for determining the nurse/patient ratio in ICU and HDU: a review of approachesJOURNAL OF NURSING MANAGEMENT, Issue 5 2004PG Dip., Renee Adomat BA (Hons) Background, A huge range of patient classification systems/tools are used in critical care units to inform workforce planning, however, they are not always applied appropriately. Many of these systems/tools were not originally developed for the purposes of workforce planning and so their use in determining the nurse:patient ratio required in critical care settings raises a number of issues for the organisation and management of these services. Aim, The aim of this paper is to review the three main assessment systems that are commonly used in critical care settings in the UK and evaluate their effectiveness in accurately determining nurse : patient ratios. If the application of these systems/tools is to enhance care, a thorough understanding of their origins and purpose is necessary. If this is lacking, then decisions relating to workload planning, particularly when calculating nurse : patient ratios, may be flawed. Conclusions, Patient dependency/classification systems and patient dependency scoring systems for severity of illness are robust measures for predicting morbidity and mortality. However, they are not accurate if used to calculate nurse : patient ratios because they are not designed to measure nursing input. Nursing intensity measures provide a useful framework for calculating the cost of providing a nursing service in critical care and can serve as a measure of nursing input, albeit a fairly basic one. However, many components of the nursing role are not ,accounted' for in these measures. Implications, The implications of these findings for the organization and management of critical care services are discussed. Careful consideration of these areas is vital if a cost efficient and cost-effective critical care service is to be delivered. [source] Accuracy of Sequential Organ Failure Assessment (SOFA) scoring in clinical practiceACTA ANAESTHESIOLOGICA SCANDINAVICA, Issue 1 2009M. TALLGREN Background: The Sequential Organ Failure Assessment (SOFA) score is used to quantify the severity of illness daily during intensive care. Our aim was to evaluate how accurately SOFA is recorded in clinical practice, and whether this can be improved by a refresher course in scoring rules. Methods: The scores recorded by physicians in a university hospital intensive care unit (ICU) were compared with the gold standard determined by two expert assessors. Data concerning all consecutive patients during two 6-week-long observation periods (baseline and after the refresher course) were compared. Results: SOFA was accurate on 75/158 (48%) patient days at baseline. The cardiovascular, coagulation, liver, and renal component scores showed excellent accuracy (,82%, weighted ,,0.92), while the neurological score showed only moderate (70%, weighted , 0.51) and the respiration score showed good accuracy (75%, weighted , 0.79). After the refresher course, the number of ,2 point errors decreased (P<0.01). Sedation precluded neurological evaluation on 135/311 (43%) days. The accuracy of the assumed neurological scores was lower than those based on timely data: 89/135 (66%, weighted , 0.55) vs. 125/176 (71%, weighted , 0.81) (P<0.01). Conclusion: Only half of the SOFA scores were accurate. In most cases, they were accurate enough to allow the recognition of organ failure and detection of change. The component scores showed good to excellent accuracy, except the neurological score. After the refresher course, the results improved slightly. The moderate accuracy of the neurological score was not amended. A simpler neurological classification tool than the Glasgow Coma Scale is needed in the ICU. [source] The use of Artificial Neural Networks to classify primate vocalizations: a pilot study on black lemursAMERICAN JOURNAL OF PRIMATOLOGY, Issue 4 2010Luca Pozzi Abstract The identification of the vocal repertoire of a species represents a crucial prerequisite for a correct interpretation of animal behavior. Artificial Neural Networks (ANNs) have been widely used in behavioral sciences, and today are considered a valuable classification tool for reducing the level of subjectivity and allowing replicable results across different studies. However, to date, no studies have applied this tool to nonhuman primate vocalizations. Here, we apply for the first time ANNs, to discriminate the vocal repertoire in a primate species, Eulemur macaco macaco. We designed an automatic procedure to extract both spectral and temporal features from signals, and performed a comparative analysis between a supervised Multilayer Perceptron and two statistical approaches commonly used in primatology (Discriminant Function Analysis and Cluster Analysis), in order to explore pros and cons of these methods in bioacoustic classification. Our results show that ANNs were able to recognize all seven vocal categories previously described (92.5,95.6%) and perform better than either statistical analysis (76.1,88.4%). The results show that ANNs can provide an effective and robust method for automatic classification also in primates, suggesting that neural models can represent a valuable tool to contribute to a better understanding of primate vocal communication. The use of neural networks to identify primate vocalizations and the further development of this approach in studying primate communication are discussed. Am. J. Primatol. 72:337,348, 2010. © 2009 Wiley-Liss, Inc. [source] Integration of colour and textural information in multivariate image analysis: defect detection and classification issuesJOURNAL OF CHEMOMETRICS, Issue 1-2 2007J. M. Prats-Montalbán Abstract In industrial processes, the detection and visualisation of defects and the development of efficient automated classification tools are strategic issues, especially when dealing with random colour textures (RCTs). This paper discusses the benefits of integrating colour and spatial (i.e. textural) information of digital RGB colour images in multivariate image analysis (MIA) to deal with these topics. Regarding the first one, a simple and computational cost-effective monitoring procedure based on colour-textural MIA merged with multivariate statistical process control (MSPC) ideas is outlined. Two novel computed images: T2 and RSS Images are proposed. The procedure is applied on digital RGB colour images from artificial stone plates. With respect to the second issue, when colour-textural MIA is used for image classification a lot of factors (e.g. pre-processing, modelling,,,) likely affecting the success rate in the classification (SRC) show up. This paper presents a methodology based on the combination of experimental design and logistic regression for choosing the best combination of factors to maximise the SRC of different types of images. Digital RGB colour images from ceramic tiles and orange fruits are used to illustrate the potential of the proposed methodology. Copyright © 2007 John Wiley & Sons, Ltd. [source] The sequence determinants of cadherin moleculesPROTEIN SCIENCE, Issue 9 2001Alexander E. Kister Abstract The sequence and structural analysis of cadherins allow us to find sequence determinants,a few positions in sequences whose residues are characteristic and specific for the structures of a given family. Comparison of the five extracellular domains of classic cadherins showed that they share the same sequence determinants despite only a nonsignificant sequence similarity between the N-terminal domain and other extracellular domains. This allowed us to predict secondary structures and propose three-dimensional structures for these domains that have not been structurally analyzed previously. A new method of assigning a sequence to its proper protein family is suggested: analysis of sequence determinants. The main advantage of this method is that it is not necessary to know all or almost all residues in a sequence as required for other traditional classification tools such as BLAST, FASTA, and HMM. Using the key positions only, that is, residues that serve as the sequence determinants, we found that all members of the classic cadherin family were unequivocally selected from among 80,000 examined proteins. In addition, we proposed a model for the secondary structure of the cytoplasmic domain of cadherins based on the principal relations between sequences and secondary structure multialignments. The patterns of the secondary structure of this domain can serve as the distinguishing characteristics of cadherins. [source] | |||||||||||||