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Nevin Manimala Statistics

The Role of Patient and Parent Education in Pediatric Cast Complications

Orthop Nurs. 2022 Sep-Oct 01;41(5):318-323. doi: 10.1097/NOR.0000000000000878.

ABSTRACT

Cast immobilization remains the standard of care in managing pediatric fractures. Cast complications often result in emergency department visits, office calls and visits, or lasting patient morbidities that burden the healthcare institution from a time and economic standpoint. The purpose of this quality improvement project was to create a multimodal cast care education protocol with an aim of decreasing cast complications over a 6-week period. Qualified patients (0-18) placed in cast immobilization received a quick response (QR) code sticker on their casts linked to a custom cast care website with text, pictures, and video instructions. Incidence of cast complications, complication type, effect(s) on workflow, and patient demographics were recorded. The complication rate declined 7.6%, but it was not statistically significant. Continuous access to clinic-specific cast instructions demonstrates decreased cast complications in pediatric populations, and this approach to patient education can be easily utilized across all medical specialties.

PMID:36166606 | DOI:10.1097/NOR.0000000000000878

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Nevin Manimala Statistics

Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209383. Online ahead of print.

ABSTRACT

While visualizations are an effective way to represent insights about information, they rarely stand alone. When designing a visualization, text is often added to provide additional context and guidance for the reader. However, there is little experimental evidence to guide designers as to what is the right amount of text to show within a chart, what its qualitative properties should be, and where it should be placed. Prior work also shows variation in personal preferences for charts versus textual representations. In this paper, we explore several research questions about the relative value of textual components of visualizations. 302 participants ranked univariate line charts containing varying amounts of text, ranging from no text (except for the axes) to a written paragraph with no visuals. Participants also described what information they could take away from line charts containing text with varying semantic content. We find that heavily annotated charts were not penalized. In fact, participants preferred the charts with the largest number of textual annotations over charts with fewer annotations or text alone. We also find effects of semantic content. For instance, the text that describes statistical or relational components of a chart leads to more takeaways referring to statistics or relational comparisons than text describing elemental or encoded components. Finally, we find different effects for the semantic levels based on the placement of the text on the chart; some kinds of information are best placed in the title, while others should be placed closer to the data. We compile these results into four chart design guidelines and discuss future implications for the combination of text and charts.

PMID:36166551 | DOI:10.1109/TVCG.2022.3209383

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Nevin Manimala Statistics

Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209405. Online ahead of print.

ABSTRACT

When an analyst or scientist has a belief about how the world works, their thinking can be biased in favor of that belief. Therefore, one bedrock principle of science is to minimize that bias by testing the predictions of one’s belief against objective data. But interpreting visualized data is a complex perceptual and cognitive process. Through two crowdsourced experiments, we demonstrate that supposedly objective assessments of the strength of a correlational relationship can be influenced by how strongly a viewer believes in the existence of that relationship. Participants viewed scatterplots depicting a relationship between meaningful variable pairs (e.g., number of environmental regulations and air quality) and estimated their correlations. They also estimated the correlation of the same scatterplots labeled instead with generic ‘X’ and ‘Y’ axes. In a separate section, they also reported how strongly they believed there to be a correlation between the meaningful variable pairs. Participants estimated correlations more accurately when they viewed scatterplots labeled with generic axes compared to scatterplots labeled with meaningful variable pairs. Furthermore, when viewers believed that two variables should have a strong relationship, they overestimated correlations between those variables by an r-value of about 0.1. When they believed that the variables should be unrelated, they underestimated the correlations by an r-value of about 0.1. While data visualizations are typically thought to present objective truths to the viewer, these results suggest that existing personal beliefs can bias even objective statistical values people extract from data.

PMID:36166548 | DOI:10.1109/TVCG.2022.3209405

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Nevin Manimala Statistics

Self-Supervised Color-Concept Association via Image Colorization

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209481. Online ahead of print.

ABSTRACT

The interpretation of colors in visualizations is facilitated when the assignments between colors and concepts in the visualizations match human’s expectations, implying that the colors can be interpreted in a semantic manner. However, manually creating a dataset of suitable associations between colors and concepts for use in visualizations is costly, as such associations would have to be collected from humans for a large variety of concepts. To address the challenge of collecting this data, we introduce a method to extract color-concept associations automatically from a set of concept images. While the state-of-the-art method extracts associations from data with supervised learning, we developed a self-supervised method based on colorization that does not require the preparation of ground truth color-concept associations. Our key insight is that a set of images of a concept should be sufficient for learning color-concept associations, since humans also learn to associate colors to concepts mainly from past visual input. Thus, we propose to use an automatic colorization method to extract statistical models of the color-concept associations that appear in concept images. Specifically, we take a colorization model pre-trained on ImageNet and fine-tune it on the set of images associated with a given concept, to predict pixel-wise probability distributions in Lab color space for the images. Then, we convert the predicted probability distributions into color ratings for a given color library and aggregate them for all the images of a concept to obtain the final color-concept associations. We evaluate our method using four different evaluation metrics and via a user study. Experiments show that, although the state-of-the-art method based on supervised learning with user-provided ratings is more effective at capturing relative associations, our self-supervised method obtains overall better results according to metrics like Earth Mover’s Distance (EMD) and Entropy Difference (ED), which are closer to human perception of color distributions.

PMID:36166543 | DOI:10.1109/TVCG.2022.3209481

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Nevin Manimala Statistics

Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209473. Online ahead of print.

ABSTRACT

Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features.

PMID:36166541 | DOI:10.1109/TVCG.2022.3209473

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Nevin Manimala Statistics

OBTracker: Visual Analytics of Off-ball Movements in Basketball

IEEE Trans Vis Comput Graph. 2022 Sep 27;PP. doi: 10.1109/TVCG.2022.3209373. Online ahead of print.

ABSTRACT

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team’s offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback.

PMID:36166529 | DOI:10.1109/TVCG.2022.3209373

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Nevin Manimala Statistics

Comorbidity-driven multi-modal subtype analysis in mild cognitive impairment of Alzheimer’s disease

Alzheimers Dement. 2022 Sep 27. doi: 10.1002/alz.12792. Online ahead of print.

ABSTRACT

BACKGROUND: Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi-modality data-driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well-known risk factors for Alzheimer’s disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD-related comorbidities along with other AD-relevant features and biomarkers.

METHODS: A total of 325 MCI subjects with 32 AD-relevant comorbidities and features were considered. Mixed-data clustering is applied to discover and compare MCI subtypes with and without including AD-related comorbidities. Finally, the relevance of each comorbidity-driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates.

RESULTS: We identified four (five) MCI subtypes: poor-, average-, good-, and best-AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity-driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity-driven MCI subtypes. Among the four comorbidity-driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test-related and plasma features, and by the proportion of comorbidities.

CONCLUSIONS: Our study indicates that AD comorbidities should be considered along with other diverse AD-relevant characteristics to better understand MCI heterogeneity.

PMID:36166485 | DOI:10.1002/alz.12792

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Nevin Manimala Statistics

Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight

PLoS Comput Biol. 2022 Sep 27;18(9):e1010512. doi: 10.1371/journal.pcbi.1010512. Online ahead of print.

ABSTRACT

Insect flight is a strongly nonlinear and actuated dynamical system. As such, strategies for understanding its control have typically relied on either model-based methods or linearizations thereof. Here we develop a framework that combines model predictive control on an established flight dynamics model and deep neural networks (DNN) to create an efficient method for solving the inverse problem of flight control. We turn to natural systems for inspiration since they inherently demonstrate network pruning with the consequence of yielding more efficient networks for a specific set of tasks. This bio-inspired approach allows us to leverage network pruning to optimally sparsify a DNN architecture in order to perform flight tasks with as few neural connections as possible, however, there are limits to sparsification. Specifically, as the number of connections falls below a critical threshold, flight performance drops considerably. We develop sparsification paradigms and explore their limits for control tasks. Monte Carlo simulations also quantify the statistical distribution of network weights during pruning given initial random weights of the DNNs. We demonstrate that on average, the network can be pruned to retain a small amount of original network weights and still perform comparably to its fully-connected counterpart. The relative number of remaining weights, however, is highly dependent on the initial architecture and size of the network. Overall, this work shows that sparsely connected DNNs are capable of predicting the forces required to follow flight trajectories. Additionally, sparsification has sharp performance limits.

PMID:36166481 | DOI:10.1371/journal.pcbi.1010512

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Nevin Manimala Statistics

Risk factors of early mortality among COVID-19 deceased patients in Addis Ababa COVID-19 care centers, Ethiopia

PLoS One. 2022 Sep 27;17(9):e0275131. doi: 10.1371/journal.pone.0275131. eCollection 2022.

ABSTRACT

BACKGROUND: Severe acute respiratory syndrome coronavirus-2 is a global health care problem with high mortality. Despite early mortality seeming alarming, data regarding factors that lead to increased early mortality of COVID 19 patients is not well-documented yet. The objective of this study was to identify the risk factors of early mortality in patients with confirmed COVID-19 infections.

METHODOLOGY: A case-control study design was employed. With this, a total of 261 COVID-19 deceased recordings were reviewed. The cases of the study were recordings of patients deceased within three days of intensive care unit admission whereas, the rest 187 were recordings of patients who died after three days of admission. Data were collected using an extraction checklist, entered into Epi data version 4.4.2.2, and analyzed by SPSS version 25. After the description, binary logistic regression was run to conduct bivariate and multivariable analyses. Finally, statistical significance was declared at p-value <0.05, and an adjusted odds ratio with a 95% confidence interval was used to report the strength of association.

RESULT: The analysis was performed on 261 (87 cases and 174 controls) recordings. About 62.5% of the participants were aged above 65 years and two-thirds were males. The presence of cardiovascular disease (AOR = 4.79, with 95%CI: 1.73, 13.27) and bronchial-asthma (AOR = 6.57; 95% CI: 1.39, 31.13) were found to have a statistically significant association with early mortality. The existence of complications from COVID-19 (AOR = 0.22; 95% CI: 0.07, 0.74) and previous history of COVID-19 infection (AOR = 0.17, 95% CI: 0.04, 0.69) were associated with decreased risk of early mortality.

CONCLUSIONS: Having cardiovascular diseases and bronchial asthma was associated with an increased risk of early mortality. Conversely, the presence of intensive care unit complications and previous history of COVID-19 infection were associated with decreased risk of early mortality.

PMID:36166445 | DOI:10.1371/journal.pone.0275131

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Nevin Manimala Statistics

Healthcare resource utilization in patients with treatment-resistant depression-A Danish national registry study

PLoS One. 2022 Sep 27;17(9):e0275299. doi: 10.1371/journal.pone.0275299. eCollection 2022.

ABSTRACT

OBJECTIVES: To investigate healthcare resource utilization (HRU) and associated costs by depression severity and year of diagnosis among patients with treatment-resistant depression (TRD) in Denmark.

METHODS: Including all adult patients with a first-time hospital contact for major depressive disorder (MDD) in 1996-2015, TRD patients were defined at the second shift in depression treatment (antidepressant medicine or electroconvulsive therapy) and matched 1:2 with non-TRD patients. The risk of utilization and amount of HRU and associated costs including medicine expenses 12 months after the TRD-defining date were reported, comparing TRD patients with non-TRD MDD patients.

RESULTS: Identifying 25,321 TRD-patients matched with 50,638 non-TRD patients, the risk of psychiatric hospitalization following TRD diagnosis was 138.4% (95%-confidence interval: 128.3-149.0) higher for TRD patients than for non-TRD MDD patients. The number of hospital bed days and emergency department (ED) visits were also higher among TRD patients, with no significant difference for somatic HRU. Among patients who incurred healthcare costs, the associated HRU costs for TRD patients were 101.9% (97.5-106.4) higher overall, and 55.2% (50.9-59.6) higher for psychiatric services than those of non-TRD patients. The relative differences in costs for TRD-patients vs non-TRD patients were greater for patients with mild depression and tended to increase over the study period (1996-2015), particularly for acute hospitalizations and ED visits.

LIMITATIONS: TRD was defined by prescription patterns besides ECT treatments.

CONCLUSION: TRD was associated with increased psychiatric-related HRU. Particularly the difference in acute hospitalizations and ED visits between TRD and non-TRD patients increased over the study period.

PMID:36166443 | DOI:10.1371/journal.pone.0275299