Search

A prior study of septicemia in our community teaching hospital demonstrated the importance of case mix categories in understanding differences in rates of septicemia seen in hospitals. This study provides a 10 year, in 1982, and a 15 year, in 1987, follow-up of septicemia from the same hospital. A substantial increase in the incidence of septicemia was noted in 1982 and 1987. Underlying illness categories continued to be important predictors of incidence of septicemia and fatality rates. The sites of infections, relative mix of community-acquired and nosocomial infections, and mix of organisms were fairly stable throughout the period. The overall incidence of septicemia increased from 34/10,000 admissions in 1970-1973 to 87/10,000 in 1982 and 103/10,000 in 1987. The major explanations for these increases are: (a) a striking increase in the use of blood cultures from 1 blood culture per 10.4 patients in 1973 to 1 blood culture per 3.3 patients in 1987; (b) an increase in the proportion of patients in the Medicare age group from 13% in 1970-1973 to 24% in 1987, with that age group accounting for 33% of the cases of septicemia in 1970-1973 and 55% in 1982 and 1987; and (c) a modest change in the case mix category of ultimately fatal underlying illness and a probable increase in the acuity of illness for most patients admitted. Parallel changes in frequency of use of blood cultures have occurred at the previously evaluated medical school teaching hospital in the same community. These same explanations are likely to be applicable for the same time period in other acute care hospitals as well. [ABSTRACT FROM AUTHOR]

The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on twodimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42 817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at hpicgs/Topic-Models-and-DimensionalityReduction-Sensitivity-Study and results as Zenodo archive at DOI:10.5281/zenodo.12772898.

Point clouds are widely used as a versatile representation of 3D entities and scenes for all scale domains and in a variety of application areas, serving as a fundamental data category to directly convey spatial features. However, due to point sparsity, lack of structure, irregular distribution, and acquisition-related inaccuracies, results of point cloud visualization are often subject to visual complexity and ambiguity. In this regard, non-photorealistic rendering can improve visual communication by reducing the cognitive effort required to understand an image or scene and by directing attention to important features. In the last 20 years, this has been demonstrated by various non-photorealistic rendering approaches that were proposed to target point clouds specifically. However, they do not use a common language or structure for assessment which complicates comparison and selection. Further, recent developments regarding point cloud characteristics and processing, such as massive data size or web-based rendering are rarely considered. To address these issues, we present a survey on non-photorealistic rendering approaches for point cloud visualization, providing an overview of the current state of research. We derive a structure for the assessment of approaches, proposing seven primary dimensions for the categorization regarding intended goals, data requirements, used techniques, and mode of operation. We then systematically assess corresponding approaches and utilize this classification to identify trends and research gaps, motivating future research in the development of effective non-photorealistic point cloud rendering methods.

Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity between the documents and supporting corpus analysis. Although the choice of the topic model, the dimensionality reduction, and their underlying hyperparameters significantly impact the resulting layout, it is unknown which particular combinations result in high-quality layouts with respect to accuracy and perception metrics. To investigate the effectiveness of topic models and dimensionality reduction methods for the spatialization of corpora as two-dimensional scatter plots (or basis for landscape-type visualizations), we present a large-scale, benchmark-based computational evaluation. Our evaluation consists of (1) a set of corpora, (2) a set of layout algorithms that are combinations of topic models and dimensionality reductions, and (3) quality metrics for quantifying the resulting layout. The corpora are given as document-term matrices, and each document is assigned to a thematic class. The chosen metrics quantify the preservation of local and global properties and the perceptual effectiveness of the two-dimensional scatter plots. By evaluating the benchmark on a computing cluster, we derived a multivariate dataset with over 45 000 individual layouts and corresponding quality metrics. Based on the results, we propose guidelines for the effective design of text spatializations that are based on topic models and dimensionality reductions. As a main result, we show that interpretable topic models are beneficial for capturing the structure of text corpora. We furthermore recommend the use of t-SNE as a subsequent dimensionality reduction.

Mastoiditis still occurs as a nonmeningeal complication of otitis media. Fever, postauricular swelling, tenderness of the ear pinna, severe otalgia and ear drainage are all typical clinical findings. Most commonly, the causative organisms are the same aerobic bacteria that are responsible for otitis media; however, anaerobic organisms must also be considered. Treatment for acute mastoiditis includes appropriate intravenous antibiotics and myringotomy for culture and drainage. Mastoidectomy is indicated if medical therapy fails.