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International Journal of Data Science (5 papers in press)
A retrospective data analysis of Legionella pneumophila diagnostic procedures and their impact on patients management: The experience of a rapid point-of-care test by Eliona Gkika, Dimosthenis Chochlakis, Yannis Tselentis, Constantin Zopounidis, Vassilis Kouikoglou, Kitsos Gkikas, ANNA PSAROULAKI Abstract: This study aims at a comparative assessment of a conventional test and a point of care test (POCT) for the diagnosis of Legionella pneumophila, by considering laboratory and clinical performance (test turnaround times (TAT), antibiotic treatment, and diagnostic efficiency, as well as, economic criteria.
A retrospective analysis was undertaken using data from the microbiology laboratories of two hospitals in Crete, Greece. We focused on hospitalized patients with clinical evidence of pneumonia and positive test for L. pneumophila (confirmed cases). Hospital A adopts a conventional serological diagnosis based on an indirect fluorescent-antibody technique (IFA) and Hospital B uses a urinary antigen test (UAT), which is a rapid POCT.
The mean TAT was 4.45 days (range 0 21) for the conventional IFA test and 0.11 days (range 0 64) for UAT. A total of 24 laboratory positive cases (11 inpatients, 13 outpatients) were identified out of 905 analyzed samples taken from 751 people. Infection was more prevalent in men, with a mean age of 61.77 years (SD=20.03; range 5 92). The mean daily hospitalization cost of confirmed cases was 127.45 for Hospital A (two in-patients with costs 91.00 and 163.90) and 79.86 for Hospital B (nine in-patients with cost range 60.00 135.00). The mean antibiotic treatment cost per patient in Hospital A was much higher than in Hospital B. Provision of a rapid laboratory diagnosis of L. pneumophila could significantly decrease time to diagnosis, improve treatment and consequently reduce the associated hospitalization charges. Keywords: Legionella pneumophila; Point of care testing; turnaround time; length of stay; cost reduction.
ANALYSIS OF WEATHER DATA USING VARIOUS REGRESSION ALGORITHMS by Jahnavi Y Abstract: Weather forecasting is a vital application in meteorology and has been one of the most challenging problems around the world. Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions. This is carried out using several regression algorithms. This paper focuses on weather analysis using various regression algorithms in data mining. There are various regression algorithms such as Linear Regression, Nonlinear Regression, Classification And Regression Tree, Multilayer Perceptron Neural Network, Support Vector Machine etc. In this work Linear Regression, Classification And Regression Tree, Multilayer Perceptron Neural Network and Support Vector Machine are used. For weather analysis various primary atmospheric parameters such as average temperature, average pressure and relative humidity are considered. The performance is analyzed using various evaluation measures. Evaluation criteria like root mean square error, mean absolute error, relative absolute error and root relative square error are used for measuring the performance of regression algorithms. By experimentation it has been observed that the error rate of Linear Regression is more than Classification And Regression Tree, the error rate of Classification And Regression Tree is more than Multilayer Perceptron. Support Vector Machine is better than Multilayer Perceptron, Classification And Regression Tree and Linear Regression. For relative root square error, Classification And Regression Tree has higher rate in evaluating training data and test data. Keywords: Multilayer Perceptron; Classification And Regression Tree; Support Vector Machine.
Analysis of Co-authorship Network Based on Some Betweenness Centrality Concepts by Divya Sindhu Lekha, Kannan Balakrishnan, Sunil Kumar R Abstract: Reliant components of a network are the connector nodes which aid in establishing a strongly connected network. Betweenness centrality of a node well captures its connecting capability. We suggest some new betweenness centrality measures which could be useful in analysing the structural connectivity of a network. In this paper we study the behaviour of collaboration in a co-authorship network, namely the NetScience network, from the perspective of these measures. We analyse the network from a micro perspective, where we consider small groups of scientists doing research in a common subdiscipline. We show that each group is formed by the influence of only one or two highly collaborating authors. Another speculation was that even though these authors are highly influential in smaller groups they do not possess notable contribution to the overall research of main discipline. Keywords: Complex networks; Network centrality; Graph theory; Betweenness center; Collaboration network; Co-authorship network.
An Application of the Logic of Explanatory Power in Rough Set Analysis: Implications for the Classification of Decision Rules by Anthony T. Odoemena Abstract: This paper uses the logic of explanatory power to address the question of uncertain decision rule classification and interpretation in rough set data analysis. A set theoretic configuration of the measure of explanatory power is introduced. The usefulness of the measure is then examined in the context of two data setsone related to car evaluation and the other related to the provision of extra educational supports. It is found that the explanatory power measure has some interesting properties that enhance the informativeness and interpretation of non-deterministic decision rules. The result of the numerical analysis shows that the explanatory power index is unique. The index can also facilitate the establishment of an objective threshold that determines whether the explanatory relevance of the premise in a given decision rule is positive, negative, or neutral. Keywords: Rough sets; explanatory power; data analysis; decision rules.
Sentiment Analysis on Organizational Resilience by Tiffany Maldonado, Ray Qing Cao, Lila Carden Abstract: By applying a sentiment analysis, we examine how firms can achieve organizational resilience by focusing on two different operational strategies in their responses to adverse events: anticipatory responses or reactionary responses. We examined 210 firms and found that firms that focus on an anticipatory strategy of investing in corporate social responsibility benefited from increased organizational resilience. We also found that firms that focus on a reactionary focus of risk management practice in their daily operations also benefited from increased organizational resilience. Furthermore, our study revealed that firms that focus on the economic and environmental aspects of corporate social responsibility and the risk assessment process benefited from higher levels of organizational resilience. Keywords: Sentiment analysis; Texting mining; Big data; Data analytics; Organizational resilience; Corporate social responsibility.