scholarly journals Visual Analytics of Tuberculosis Detection Rat Performance

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Joan Jonathan ◽  
Camilius Sanga ◽  
Magesa Mwita ◽  
Georgies Mgode
Keyword(s):  
Random Forest ◽  
Visual Analytics ◽  
Detection Performance ◽  
Large Sample Size ◽  
Global Challenge ◽  
Detection Technology ◽  
Interactive Visualizations ◽  
Diagnostic Approaches ◽  
Accuracy Check ◽  
Pouched Rats

The diagnosis of tuberculosis (TB) disease remains a global challenge, and the need for innovative diagnostic approaches is inevitable. Trained African giant pouched rats are the scent TB detection technology for operational research. The adoption of this technology is beneficial to countries with a high TB burden due to its cost-effectiveness and speed than microscopy. However, rats with some factors perform better. Thus, more insights on factors that may affect performance is important to increase rats’ TB detection performance. This paper intends to provide understanding on the factors that influence rats TB detection performance using visual analytics approach. Visual analytics provide insight of data through the combination of computational predictive models and interactive visualizations. Three algorithms such as Decision tree, Random Forest and Naive Bayes were used to predict the factors that influence rats TB detection performance. Hence, our study found that age is the most significant factor, and rats of ages between 3.1 to 6 years portrayed potentiality. The algorithms were validated using the same test data to check their prediction accuracy. The accuracy check showed that the random forest outperforms with an accuracy of 78.82% than the two. However, their accuracies difference is small. The study findings may help rats TB trainers, researchers in rats TB and Information system, and decision makers to improve detection performance. This study recommends further research that incorporates gender factors and a large sample size.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Vermont: a multi-perspective visual interactive platform for mutational analysis

2017 ◽  
Author(s):  
Alexandre V Fassio ◽  
Pedro M Martins ◽  
Samuel da S Guimarães ◽  
Sócrates S A Junior ◽  
Vagner S Ribeiro ◽  
...  
Keyword(s):  
Protein Function ◽  
Visual Analytics ◽  
Large Scale ◽  
Mutational Analysis ◽  
Structural Parameters ◽  
Point Mutations ◽  
Huge Amount ◽  
Interactive Visualizations ◽  
And Function ◽  
The Impact

AbstractBackgroundA huge amount of data about genomes and sequence variation is available and continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association and effects on protein structure and function. Therefore, reliable computational approaches are needed to support the understanding of mutations and their impacts. Here, we present VERMONT 2.0, a visual interactive platform that combines sequence and structural parameters with interactive visualizations to make the impact of protein point mutations more understandable.ResultsWe aimed to contribute a novel visual analytics oriented method to analyze and gain insight on the impact of protein point mutations. To assess the ability of VERMONT to do this, we visually examined a set of mutations that were experimentally characterized to determine if VERMONT could identify damaging mutations and why they can be considered so.ConclusionsVERMONT allowed us to understand mutations by interpreting position-specific structural and physicochemical properties. Additionally, we note some specific positions we believe have an impact on protein function/structure in the case of mutation.

scholarly journals Human Detection via Image Denoising for 5G-Enabled Intelligent Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hui Li ◽  
Hang Zhou ◽  
Xiaoguo Liang ◽  
Fen Cai ◽  
Lingwei Xu ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Multiple Scales ◽  
Autonomous Driving ◽  
Detection Performance ◽  
Feature Representation ◽  
Human Detection ◽  
Intelligent Video Surveillance ◽  
Video Images ◽  
Detection Technology ◽  
Efficient Transmission

5G technology strongly supports the development of various intelligent applications, such as intelligent video surveillance and autonomous driving. And the human detection technology in intelligent video surveillance has also ushered in new challenges. A number of video images will be compressed for efficient transmission; the resulting incomplete feature representation of images will drop the human detection performance. Therefore, in this work, we propose a new human detection method based on compressed denoising. We exploit the quality factor in the compressed image and incorporate the pixel_shuffle inverse transform based on FFDNet to effectively improve the performance of image compression denoising, then HRNet and HRFPN are used to extract and fuse high-resolution features of denoised images, respectively, to obtain high-quality feature representation, and finally, a cascaded object detector is used for classification and bounding box regression to further improve object detection performance. At last, the experimental results on PASCAL VOC show that the proposed method effectively removes the compression noise and further detects human objects with multiple scales and different postures. Compared with the state-of-the-art methods, our method achieved better detection performance and is, therefore, more suited for human detection tasks.

scholarly journals Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency

2020 ◽  
Vol 27 (5) ◽  
pp. 783-787 ◽  
Author(s):  
Andrew Stirling ◽  
Tracy Tubb ◽  
Emily S Reiff ◽  
Chad A Grotegut ◽  
Jennifer Gagnon ◽  
...  
Keyword(s):  
Operating Room ◽  
Patient Outcomes ◽  
Visual Analytics ◽  
Project Success ◽  
Clinical Care ◽  
Health Record ◽  
Operating Room Efficiency ◽  
Birth Center ◽  
Interactive Visualizations ◽  
And Control

Abstract Objective While electronic health record (EHR) systems store copious amounts of patient data, aggregating those data across patients can be challenging. Visual analytic tools that integrate with EHR systems allow clinicians to gain better insight and understanding into clinical care and management. We report on our experience building Tableau-based visualizations and integrating them into our EHR system. Materials and Methods Visual analytic tools were created as part of 12 clinician-initiated quality improvement projects. We built the visual analytic tools in Tableau and linked it within our EPIC environment. We identified 5 visual themes that spanned the various projects. To illustrate these themes, we choose 1 exemplary project which aimed to improve obstetric operating room efficiency. Results Across our 12 projects, we identified 5 visual themes that are integral to project success: scheduling & optimization (in 11/12 projects); provider assessment (10/12); executive assessment (8/12); patient outcomes (7/12); and control and goal charts (2/12). Discussion Many visualizations share common themes. Identification of these themes has allowed our internal team to be more efficient and directed in developing visualizations for future projects. Conclusion Organizing visual analytics into themes can allow informatics teams to more efficiently provide visual products to clinical collaborators.

scholarly journals VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 85
Author(s):  
Maede S. Nouri ◽  
Daniel J. Lizotte ◽  
Kamran Sedig ◽  
Sheikh S. Abdullah
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Visual Analytics ◽  
Machine Learning Techniques ◽  
Medical Record Data ◽  
Making Sense ◽  
Electronic Medical Record Data ◽  
Record Data ◽  
Interactive Visualizations ◽  
Care Patterns

Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.

scholarly journals Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics

2021 ◽  
Author(s):  
Ratanond Koonchanok ◽  
Parul Baser ◽  
Abhinav Sikharam ◽  
Nirmal Kumar Raveendranath ◽  
Khairi Reda
Keyword(s):  
Visual Analytics ◽  
Exploratory Data Analysis ◽  
Visual Analysis ◽  
Data Driven ◽  
Belief Elicitation ◽  
Mixed Analysis ◽  
Limited Support ◽  
Interactive Visualizations ◽  
Exploratory Data ◽  
Confirmatory Analysis

Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants' cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design.

scholarly journals Visual Analytics for Electronic Health Records: A Review

Informatics ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 12
Author(s):  
Neda Rostamzadeh ◽  
Sheikh S. Abdullah ◽  
Kamran Sedig
Keyword(s):  
Clinical Data ◽  
Cognitive Processes ◽  
Visual Analytics ◽  
Information Overload ◽  
Research Papers ◽  
Interactive Visualizations ◽  
Electronic Health ◽  
Key Dimensions ◽  
Untapped Resource

The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work.

scholarly journals Random Forest Similarity Maps: A Scalable Visual Representation for Global and Local Interpretation

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2862
Author(s):  
Dipankar Mazumdar ◽  
Mário Popolin Neto ◽  
Fernando V. Paulovich
Keyword(s):  
Random Forest ◽  
Visual Analytics ◽  
Model Errors ◽  
Interactive Nature ◽  
Complex Models ◽  
Similarity Maps ◽  
Interpretable Models ◽  
Feature Values ◽  
Visualization Techniques ◽  
Global And Local

Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in various domains. However, as ML algorithms surge, the need for transparent and interpretable models becomes essential. Visual representations have shown to be instrumental in addressing such an issue, allowing users to grasp models’ inner workings. Despite their popularity, visualization techniques still present visual scalability limitations, mainly when applied to analyze popular and complex models, such as Random Forests (RF). In this work, we propose Random Forest Similarity Map (RFMap), a scalable interactive visual analytics tool designed to analyze RF ensemble models. RFMap focuses on explaining the inner working mechanism of models through different views describing individual data instance predictions, providing an overview of the entire forest of trees, and highlighting instance input feature values. The interactive nature of RFMap allows users to visually interpret model errors and decisions, establishing the necessary confidence and user trust in RF models and improving performance.

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