scholarly journals Visual Analytics in Delirium Management

2021 ◽  
Author(s):  
Werner O. Hackl ◽  
Michael Netzer ◽  
Renate Nantschev ◽  
Michael Schaller ◽  
Elske Ammenwerth
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Visual Analytics ◽  
Time Course ◽  
Safety Issue ◽  
Current Data ◽  
Data Set ◽  
Patient Safety Issue ◽  
Delirium Management ◽  
Visualization Techniques

Background: Delirium is a patient safety issue that often occurs within the population of elderly people. As delirium may be characterized by fluctuating progress, the aim of this work is to find methods to visualize the occurrence of delirium over time in different patient stays in gerontopsychatric settings. Methods: We analyzed current data mining visualization techniques for clinical research using a delirium data set collected in a gerontopsychatric setting. Results: We identified heatmaps and dendrograms resulting from hierarchical clustering as a suitable visualization method. Conclusion: Heat maps with hierarchical clustering are a suitable data mining tool or visualization technique to study delirium cases in the time course of patient stays.

scholarly journals Performance Comparison with Hierarchical and Partitional Clustering Methods

2021 ◽  
Vol 20 ◽  
pp. 177-184
Author(s):  
Ozer Ozdemir ◽  
Simgenur Cerman
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Performance Comparison ◽  
Hierarchical Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Partitional Clustering ◽  
Statistical Programming ◽  
Using Data

In data mining, one of the commonly-used techniques is the clustering. Clustering can be done by the different algorithms such as hierarchical, partitioning, grid, density and graph based algorithms. In this study first of all the concept of data mining explained, then giving information the aims of using data mining and the areas of using and then clustering and clustering algorithms that used in data mining are explained theoretically. Ultimately within the scope of this study, "Mall Customers" data set that taken from Kaggle database, based partitioned clustering and hierarchical clustering algorithms aimed at the separation of clusters according to their costumers features. In the clusters obtained by the partitional clustering algorithms, the similarity within the cluster is maximum and the similarity between the clusters is minimum. The hierarchical clustering algorithms is based on the gathering of similar features or vice versa. The partitional clustering algorithms used; k-means and PAM, hierarchical clustering algorithms used; AGNES and DIANA are algorithms. In this study, R statistical programming language was used in the application of algorithms. At the end of the study, the data set was run with clustering algorithms and the obtained analysis results were interpreted.

scholarly journals A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

2021 ◽  
pp. 412-418
Author(s):  
Aastha Gupta ◽  
Himanshu Sharma ◽  
Anas Akhtar
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Analytical Techniques ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
The Many ◽  
Data Elements

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering

Application of Data Mining Algorithms in Determination of Voting Tendencies in Turkey

2021 ◽  
pp. 134-149
Author(s):  
Ali Bayır ◽  
Sevinç Gülseçen ◽  
Gökhan Türkmen
Keyword(s):  
Data Mining ◽  
Current Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Human Behaviors ◽  
Political Sciences ◽  
Data Mining Algorithms ◽  
Number Of Factors ◽  
Mining Algorithms

Political elections are influenced by a number of factors such as political tendencies, voters' perceptions, and preferences. The results of a political election could also be based on specific attributes of candidates: age, gender, occupancy, education, etc. Although it is very difficult to understand all the factors which could have influenced the outcome of the election, many of the attributes mentioned above could be included in a data set, and by using current data mining techniques, undiscovered patterns can be revealed. Despite unpredictability of human behaviors and/or choices involved, data mining techniques still could help in predicting the election outcomes. In this study, the results of the survey prepared by KONDA Research and Consultancy Company before 2011 elections in Turkey were used as raw data. This study may help in understanding how data mining methods and techniques could be used in political sciences research. The study may also reveal whether voting tendencies in elections could be a factor for the outcome of the election.

HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

2019 ◽  
Vol 14 (2) ◽  
pp. 148-156
Author(s):  
Nighat Noureen ◽  
Sahar Fazal ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Hierarchical Clustering ◽  
Real Data ◽  
Cell Types ◽  
Computational Scheme ◽  
Data Set ◽  
Chromatin States ◽  
Functional Regions ◽  
Visualization Strategy ◽  
Hidden States ◽  
Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

scholarly journals Visual Analysis of Biomarkers Reveals Differences in Lipid Profiles and Liver Enzymes before and after Gastric Sleeve and Bypass

Obesity Facts ◽  
2021 ◽  
pp. 1-11
Author(s):  
Marijn Marthe Georgine van Berckel ◽  
Saskia L.M. van Loon ◽  
Arjen-Kars Boer ◽  
Volkher Scharnhorst ◽  
Simon W. Nienhuijs
Keyword(s):  
Bariatric Surgery ◽  
Visual Analytics ◽  
Biochemical Markers ◽  
Visual Analysis ◽  
Large Population ◽  
High Volume ◽  
Cholesterol Concentration ◽  
Health State ◽  
Data Set ◽  
Before And After

<b><i>Introduction:</i></b> Bariatric surgery results in both intentional and unintentional metabolic changes. In a high-volume bariatric center, extensive laboratory panels are used to monitor these changes pre- and postoperatively. Consecutive measurements of relevant biochemical markers allow exploration of the health state of bariatric patients and comparison of different patient groups. <b><i>Objective:</i></b> The objective of this study is to compare biomarker distributions over time between 2 common bariatric procedures, i.e., sleeve gastrectomy (SG) and gastric bypass (RYGB), using visual analytics. <b><i>Methods:</i></b> Both pre- and postsurgical (6, 12, and 24 months) data of all patients who underwent primary bariatric surgery were collected retrospectively. The distribution and evolution of different biochemical markers were compared before and after surgery using asymmetric beanplots in order to evaluate the effect of primary SG and RYGB. A beanplot is an alternative to the boxplot that allows an easy and thorough visual comparison of univariate data. <b><i>Results:</i></b> In total, 1,237 patients (659 SG and 578 RYGB) were included. The sleeve and bypass groups were comparable in terms of age and the prevalence of comorbidities. The mean presurgical BMI and the percentage of males were higher in the sleeve group. The effect of surgery on lowering of glycated hemoglobin was similar for both surgery types. After RYGB surgery, the decrease in the cholesterol concentration was larger than after SG. The enzymatic activity of aspartate aminotransferase, alanine aminotransferase, and alkaline phosphate in sleeve patients was higher presurgically but lower postsurgically compared to bypass values. <b><i>Conclusions:</i></b> Beanplots allow intuitive visualization of population distributions. Analysis of this large population-based data set using beanplots suggests comparable efficacies of both types of surgery in reducing diabetes. RYGB surgery reduced dyslipidemia more effectively than SG. The trend toward a larger decrease in liver enzyme activities following SG is a subject for further investigation.

scholarly journals Effect of Speech-to-Noise Ratio and Luminance on a Range of Current and Potential Pupil Response Measures to Assess Listening Effort

2021 ◽  
Vol 25 ◽  
pp. 233121652110093
Author(s):  
Patrycja Książek ◽  
Adriana A. Zekveld ◽  
Dorothea Wendt ◽  
Lorenz Fiedler ◽  
Thomas Lunner ◽  
...  
Keyword(s):  
Time Course ◽  
Principal Component ◽  
Growth Curve Analysis ◽  
Listening Effort ◽  
Data Set ◽  
Holistic View ◽  
Hearing Research ◽  
Speech In Noise ◽  
Noise Test ◽  
Noise Ratio

In hearing research, pupillometry is an established method of studying listening effort. The focus of this study was to evaluate several pupil measures extracted from the Task-Evoked Pupil Responses (TEPRs) in speech-in-noise test. A range of analysis approaches was applied to extract these pupil measures, namely (a) pupil peak dilation (PPD); (b) mean pupil dilation (MPD); (c) index of pupillary activity; (d) growth curve analysis (GCA); and (e) principal component analysis (PCA). The effect of signal-to-noise ratio (SNR; Data Set A: –20 dB, –10 dB, +5 dB SNR) and luminance (Data Set B: 0.1 cd/m2, 360 cd/m2) on the TEPRs were investigated. Data Sets A and B were recorded during a speech-in-noise test and included TEPRs from 33 and 27 normal-hearing native Dutch speakers, respectively. The main results were as follows: (a) A significant effect of SNR was revealed for all pupil measures extracted in the time domain (PPD, MPD, GCA, PCA); (b) Two time series analysis approaches (GCA, PCA) provided modeled temporal profiles of TEPRs (GCA); and time windows spanning subtasks performed in a speech-in-noise test (PCA); and (c) All pupil measures revealed a significant effect of luminance. In conclusion, multiple pupil measures showed similar effects of SNR, suggesting that effort may be reflected in multiple aspects of TEPR. Moreover, a direct analysis of the pupil time course seems to provide a more holistic view of TEPRs, yet further research is needed to understand and interpret its measures. Further research is also required to find pupil measures less sensitive to changes in luminance.

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

scholarly journals Use of Continuous Infusion Pumps During Radiation Treatment

2013 ◽  
Vol 9 (2) ◽  
pp. 107-111 ◽  
Author(s):  
Kate Bak ◽  
Eric Gutierrez ◽  
Elizabeth Lockhart ◽  
Michael Sharpe ◽  
Esther Green ◽  
...  
Keyword(s):  
Patient Safety ◽  
Radiation Exposure ◽  
Continuous Infusion ◽  
Radiation Treatment ◽  
Safety Issue ◽  
Infusion Pumps ◽  
Additional Testing ◽  
Infusion Devices ◽  
Patient Safety Issue

The varied results of radiation exposure on infusion devices suggest that additional testing should be carried out to determine the limits of dose exposure, and to raise awareness around this patient safety issue.

Differential expression of TNF-α, IL-6, and IGF-1 by graded mechanical stress in normal rat myocardium

2002 ◽  
Vol 282 (3) ◽  
pp. H926-H934 ◽  
Author(s):  
Emiliano A. Palmieri ◽  
Giulio Benincasa ◽  
Francesca Di Rella ◽  
Cosma Casaburi ◽  
Maria G. Monti ◽  
...  
Keyword(s):  
Time Course ◽  
De Novo ◽  
Mechanical Stretch ◽  
Progressive Increase ◽  
Compensatory Hypertrophy ◽  
Current Data ◽  
Rat Myocardium ◽  
Tnf Α ◽  
Normal Rat ◽  
Necrosis Factor

An isovolumic normal rat heart Langendorff model was used to examine the effects of moderate (15 mmHg) and severe (35 mmHg) mechanical stretch on the time course (from 0 to 60 min) of myocardial expression of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and insulin-like growth factor (IGF)-1 and their cognate receptors. After 10 min of moderate stretch, TNF-α was de novo expressed, whereas constitutive IL-6 and IGF-1 levels were slightly upregulated; no further changes occurred up to 60 min. In comparison, severe stretch resulted in a higher and progressive increase in TNF-α, IL-6, and IGF-1 expression up to 20 min. After 20 min, whereas TNF-α expression further increased, IL-6 and IGF-1 levels progressively reduced to values lower than those observed under moderate stretch and in unstretched (5 mmHg) control myocardium (IL-6). Mechanical stretch did not significantly alter the expression of the cognate receptors. Indeed, the TNF-α receptor (p55) tended to be progressively upregulated under severe stretch over time. The current data provide the first demonstration that TNF-α, IL-6, and IGF-1 ligand-receptor systems are differentially expressed within the normal rat myocardium in response to graded mechanical stretch. Such findings may have potential implications with regard to compensatory hypertrophy and failure.

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