Use Of Visual Analytics Research Articles

Visualizing Semantic Structure of a Clinical Text Document

The increasing use of Electronic Health Records (EHRs) in healthcare delivery settings has led to increase availability of electronic clinical data. They generate a lot of patients’ clinical data each day, requiring physicians to review them to find clinically relevant information of different patients during care episodes. The availability of electronically collected healthcare data has created the need of computational tools to analyze them. One of the types of data which doctors have access to is clinical notes that resides in electronic health records. These notes are useful as they provide comprehensive information about patients’ health histories with many practical uses. For example, doctors always review these notes during care episodes to appraise themselves about the health history of a patient. These reviews are currently manual where a doctor reads a patient’s chart while looking for specific clinical information. Without the proper support, this manual process leads to information overload and increases physician cognitive workload. Current electronic health records (EHRs) do not provide support to help physicians reduce cognitive workload when completing clinical tasks. This is especially true for long clinical documents which require quick review at the point of care. The growing amount of clinical documentation available in EHRs has arose the need of tools that support synthesize of information in EHRs. The use of visual analytics to explore healthcare data is one such research direction to address this problem. However, existing visualization techniques are mainly based on structured electronic health record and rarely support therapeutic activities. Therefore, visualization of unstructured clinical records to support clinical practice is required. In this paper we propose a unique approach for graphically representing and visualizing the semantic structure of a clinical text document to aid doctors in reviewing electronic clinical notes. A user evaluation demonstrates that the proposed method for visualizing and navigating a document’s semantic structure facilitates a user’s document information exploration.

Experts\u2019 perceptions on the use of visual analytics for complex mental healthcare planning: an exploratory study

BackgroundHealth experts including planners and policy-makers face complex decisions in diverse and constantly changing healthcare systems. Visual analytics may play a critical role in supporting analysis of complex healthcare data and decision-making. The purpose of this study was to examine the real-world experience that experts in mental healthcare planning have with visual analytics tools, investigate how well current visualisation techniques meet their needs, and suggest priorities for the future development of visual analytics tools of practical benefit to mental healthcare policy and decision-making.MethodsHealth expert experience was assessed by an online exploratory survey consisting of a mix of multiple choice and open-ended questions. Health experts were sampled from an international pool of policy-makers, health agency directors, and researchers with extensive and direct experience of using visual analytics tools for complex mental healthcare systems planning. We invited them to the survey, and the experts’ responses were analysed using statistical and text mining approaches.ResultsThe forty respondents who took part in the study recognised the complexity of healthcare systems data, but had most experience with and preference for relatively simple and familiar visualisations such as bar charts, scatter plots, and geographical maps. Sixty-five percent rated visual analytics as important to their field for evidence-informed decision-making processes. Fifty-five percent indicated that more advanced visual analytics tools were needed for their data analysis, and 67.5% stated their willingness to learn new tools. This was reflected in text mining and qualitative synthesis of open-ended responses.ConclusionsThis exploratory research provides readers with the first self-report insight into expert experience with visual analytics in mental healthcare systems research and policy. In spite of the awareness of their importance for complex healthcare planning, the majority of experts use simple, readily available visualisation tools. We conclude that co-creation and co-development strategies will be required to support advanced visual analytics tools and skills, which will become essential in the future of healthcare.Graphical abstract

VISUAL ANALYTICS EVALUATION PROCESS: PRACTICE GUIDELINES FOR COMPLEX DOMAIN

Visual analytics fundamentally influences the analytical process. Without proper guidance, the use of visual analytics in complex domain can become an obstacle that hides the usefulness of analytical data. Since evaluation is the only way to identify the effectiveness of visual analytics to represent analytical outcomes, misconception of the evaluation process will bury the relevancy of visualization to support valuable decision. Recently, the nature domain of data has changed and we are now dealing with data that is massive, ambiguous, and dynamic, is often processed in real time. Hence, the data is more complex and cognitive activities that visual analytics facilitates are also getting more complex. Thus, this research revisits the way to evaluate visual analytics in complex cognitive conditions that are natural, uncertain and context dependent. Governed by the Design Science Research Methodology (DSRM), there are three phases involve during the evaluation process; i). demonstration, ii). evaluation and iii). communication. The design process is embedded with the human-activity centered design approach to gain better understanding on the visual analytics users and the complex cognitive activities involved. Thus, the research proposes Focus Group Observation method in conducting the evaluation in authentic setting. By offering a set of evaluation recommendations, this research aims to enhance visual analytics among users. It also recommends the evaluation criteria, sampling strategy and participation, focus group tasks

NemoViz: a visual interactive system for atomistic simulations design

IntroductionGiven that the nanoscale regime has been reached, atomistic simulations are being used as predictive tools on a nanoscopic scale in nanoelectronics, materials science, and computational fluid dynamics. These simulations are generally supported by complex and specific simulation engines that require a deep knowledge of the engine as well as the field. The inputs required by these engines are usually described in long-text files, and their composition is error prone. These files contain details about the corresponding materials, geometries, algorithms, initial values, etc. The introduction of visualizations to the simulation creation process could alleviate some common user issues with simulation creation, in particular regarding the input-text files. In other fields, visual analytics have been used to facilitate user interaction with complex data. This work features a case study on the creation of a nanoelectronic device simulation and provides evidence of how the use of visual analytics reduces the cognitive complexity of defining an atomistic simulation.Case descriptionNEMO5 is a tool designed to simulate the electronic properties of nanoelectronic devices on an atomistic level. In this work we introduce NemoViz, an interactive visualization tool that enables users to define the simulation inputs required by NEMO5. Regular NEMO5 users were exposed to NemoViz, and users’ effectiveness and efficiency was measured while debugging the input for a simulation.Discussion and EvaluationThe results of this work show that NemoViz reduced the time that users spent defining the inputs of a NEMO5 simulation. When using NemoViz, Expert NEMO5 users detected errors twice as fast as when NemoViz was not used, and non-expert NEMO5 users were able to detect errors as effectively and efficiently as expert users.ConclusionsThese results suggest that the use of visual analytics as a simulation design process tool reduces the cognitive load of complex simulators such as NEMO5. Users’ interaction with visualization facilitates their understanding of output results and input descriptors, which may lead to new research codes from their widespread user base.

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities.

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages-design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

Advocating the use of visual analytics in the context of BMS data

A prototype of interactive data visualization is proposed for Building Management Systems (BMS). The aim is to enhance traditional data presentation format such as graphs and tables to support expert users in early open-ended exploration of building data. Based on data recorded over 3 years in a real office building in Switzerland, a case study is conducted involving 12 practicing expert users with engineering background. Both exploratory and qualitative in nature, this evaluation allows to : (1) show the ability of expert users to use advanced visualization tools to derive meaningful conclusions in limited time, (2) demonstrate the relevance of visual analytics results for the building physics field.

Visual Analytics as an Enabler for Manufacturing Process Decision-making

The goal of an optimal manufacturing process is to maximize product performance while minimizing cost, time, and waste. A critical component of this optimization is the appropriate selection of process parameters. While central physical concepts often serve as a starting point, specific parameter selection is frequently done manually, based on operator skill, experience, and intuition. As a result, process optimization is often iterative, non-repeatable, and lacking in traceability. Further, there is no fundamental insight gained into the relationship between process parameter selection and critical process outputs. This paper explores the use of visual analytics as an enabler for manufacturing process decision making. An emerging science, visual analytics couples analytical reasoning with the substantial capability of the human brain to rapidly internalize and understand data that is presented visually. Through the use of interactive interfaces, visual analytics provides a mechanism through which the operator, engineer, and decision-maker can cooperate in real-time with both simulation, experimental, and operational data, facilitating trade studies, what-if analysis, and providing crucial insight into correlations and relationships that drive process optimization. As an exemplar, the concept of visual analytics is applied to the simulation of a notional high pressure die casting process, with the goal of gaining insight into those parameters that contribute to high scrap rates, particularly air entrapment.

An Enterprise Approach for Pathology Reporting Utilizing PDF Functionality for the Electronic Medical Record

Background: Shortcomings of plain text renderings of pathology reports in the electronic medical records (EMRs) are widely known. As the pathology reports grow in complexity, Portable Document Format (PDF) renderings of pathology reports are seen as a flexible solution. Since 3/2014, our group has successfully implemented architecture for PDF functionality for pathology report integration into the EMR. Design: We outlined our architectural framework. Within our architectural framework, pathology reports are first generated as MS Word documents. They are then exported into both HL7 and PDF formats in the EMR. The clinicians may then choose to view either format. The HL7 interface message contains the file location path of the PDF, and the PDF reports are stored on the hospital servers for display retrieval within the EMR. As the HL7 feed is maintained, data can still be extracted into the institutional data warehouse (IDW). The retained HL7 rendering of the report allows copy/pasting of the pathology report into clinical documents. A reconciliation report is generated to match the transmissions sent with those received. Results: The number of PDF reports sent across the interface from 3/2014 to 5/2015 total 191,868. The monthly reports sent range from 11,449 to 14,422 per month, with a median & average of 12,419 and 12,791, respectively. Conclusions: PDF renderings of pathology reports have been successfully integrated into the EMR system. PDF reports bypass errors that may occur when HL7 strips the formatting from MS Word documents, such as blood counts or gene mutation lists. Use of visual analytics is also possible with PDF reports. Most commercially available EMRs have the capability of displaying PDF documents. We hope that our experience can serve as a framework for PDF functionality in EMRs.

Visual Analytics of Twitter Conversations about Corporate Sponsors of FC Barcelona and Juventus at the 2015 UEFA Final

Until now, little research has been conducted to analyse Twitter conversations about the corporate sponsors of football clubs. The conventional and most widely used method has been to use content analysis to assess the sentiment of the tweets that were sent. However, this approach may be inadequate because sports fans may be unlikely to mention a corporate sponsor in the text they tweet. This study demonstrates the use of visual analytics to assess conversations about corporate sponsors by examining the images people tweet.

Use of Information Visualization Methods Eliminating Cross Talk in Multiple Sensing Units Investigated for a Light-Addressable Potentiometric Sensor

The integration of nanostructured films containing biomolecules and silicon-based technologies is a promising direction for reaching miniaturized biosensors that exhibit high sensitivity and selectivity. A challenge, however, is to avoid cross talk among sensing units in an array with multiple sensors located on a small area. In this letter, we describe an array of 16 sensing units of a light-addressable potentiometric sensor (LAPS), which was made with layer-by-layer (LbL) films of a poly(amidomine) dendrimer (PAMAM) and single-walled carbon nanotubes (SWNTs), coated with a layer of the enzyme penicillinase. A visual inspection of the data from constant-current measurements with liquid samples containing distinct concentrations of penicillin, glucose, or a buffer indicated a possible cross talk between units that contained penicillinase and those that did not. With the use of multidimensional data projection techniques, normally employed in information visualization methods, we managed to distinguish the results from the modified LAPS, even in cases where the units were adjacent to each other. Furthermore, the plots generated with the interactive document map (IDMAP) projection technique enabled the distinction of the different concentrations of penicillin, from 5 mmol L(-1) down to 0.5 mmol L(-1). Data visualization also confirmed the enhanced performance of the sensing units containing carbon nanotubes, consistent with the analysis of results for LAPS sensors. The use of visual analytics, as with projection methods, may be essential to handle a large amount of data generated in multiple sensor arrays to achieve high performance in miniaturized systems.

The Science of Analytic Reporting

The challenge of visually communicating analysis results is central to the ability of visual analytics tools to support decision making and knowledge construction. The benefit of emerging visual methods will be improved through more effective exchange of the insights generated through the use of visual analytics. This article outlines the major requirements for next-generation reporting systems in terms of eight major research needs: the development of best practices, design automation, visual rhetoric, context and audience, connecting analysis to presentation, evidence and argument, collaborative environments and interactive and dynamic documents. It also describes an emerging technology called Active Products that introduces new techniques for analytic process capture and dissemination.