Improving pain communication between limited English-speaking Hmong patients, medical interpreters, and health care providers in primary care: A pilot study.

This pilot study assessed the feasibility of implementing a pain assessment information visualization (InfoViz) tool to address cultural and language barriers among limited English proficiency (LEP) Hmong patients in primary care. We used a static group comparison design to collect data from 20 patient, interpreter, and provider triads under usual care (i.e., interpreter using verbal pain descriptions), followed by another 20 triads under the intervention (i.e., interpreter using verbal pain descriptions and the InfoViz tool). Feasibility outcomes included recruitment and retention rates, InfoViz tool completion, acceptability, and fidelity. We also assessed mutual understanding (MU) and pain electronic health record (EHR) documentation. Descriptive data were calculated and thematic analysis was conducted. Thirty-six LEP Hmong patients (n = 29 female, mean age = 59.03), 27 providers (n = 15 female), and four interpreters participated in this study. The patient recruitment rate was 18% while the retention rate was 81%. Interpreter recruitment rate was 80%, and 75% for retention rate. The intervention fidelity mean score was 83%. In the intervention condition, patient-provider MU of pain severity improved by 30%, coupled with a 28% increase in pain severity EHR documentation compared to usual care. While communication of pain quality did not improve, there was a higher mean number of pain descriptors (3.31 in the intervention vs. 1.79 in usual care) in EHR documentation. All participants had a positive experience with the tool, reporting it as valuable with 100% completeness of all tools. Findings revealed the tool was acceptable and feasible to use among LEP patients-interpreters-providers, providing support for an efficacy study.

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology.

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

Assigning trend-based conservation status despite high uncertainty.

Estimates of temporal trends in species' occupancy are essential for conservation policy and planning, but limitations to the data and models often result in very high trend uncertainty. A critical source of uncertainty that degrades scientific credibility is that caused by disagreement among studies or models. Modelers are aware of this uncertainty but usually only partially estimate it and communicate it to decision makers. At the same time, there is growing awareness that full disclosure of uncertainty is critical for effective translation of science into policies and plans. But what are the most effective approaches to estimating uncertainty and communicating uncertainty to decision makers? We explored how alternative approaches to estimating and communicating uncertainty of species trends could affect decisions concerning conservation status of freshwater fishes. We used ensemble models to propagate trend uncertainty within and among models and communicated this uncertainty with categorical distributions of trend direction and magnitude. All approaches were designed to fit an established decision-making system used to assign species conservation status by the New Zealand government. Our results showed how approaches that failed to fully disclose uncertainty, while simplifying the information presented, could hamper species conservation or lead to ineffective decisions. We recommend an approach that was recently used effectively to communicate trend uncertainty to a panel responsible for setting the conservation status of New Zealand's freshwater fishes.

Designación del estado de conservación basada en tendencias a pesar de gran incertidumbre Resumen Las estimaciones de las tendencias temporales de la presencia de especies son esenciales para la planeación de la conservación y sus políticas, pero las limitaciones de los datos y los modelos suelen derivar en una incertidumbre muy elevada en cuanto a las tendencias. Los desacuerdos entre los estudios y los modelos son una fuente importante de incertidumbre que contribuye a la degradación de la credibilidad científica. Los modeladores están conscientes de esta incertidumbre, pero casi nunca la estiman o comunican por completo a los responsables de las decisiones. Al mismo tiempo, cada vez hay mayor conciencia de que divulgar esta incertidumbre es importante para que la ciencia se traduzca efectivamente en políticas y planes. ¿Pero cuáles son las estrategias más efectivas para estimar la incertidumbre y comunicarla a los responsables de las decisiones? Exploramos cómo las estrategias alternativas para estimar y comunicar la incertidumbre que rodea a las tendencias de las especies podría afectar las decisiones con respecto al estado de conservación de los peces de agua dulce. Usamos modelos de conjuntos para propagar la incertidumbre dentro y entre modelos y comunicamos esta incertidumbre con distribuciones categóricas de la dirección y magnitud de la tendencia. Diseñamos todas las estrategias para que se ajustaran a un sistema establecido de toma de decisiones que usa el gobierno de Nueva Zelanda para designar el estado de conservación de las especies. Nuestros resultados mostraron cómo las estrategias que no divulgaron por completo la incertidumbre, mientras simplificaban la información presentada, podrían dificultar la conservación de las especies o llevar a decisiones poco efectivas. Recomendamos una estrategia que se usó recientemente para comunicar eficientemente la incertidumbre de las tendencias a un panel responsable de establecer el estado de conservación de los peces de agua dulce de Nueva Zelanda.

Feasibility and acceptability of using information visualizations to improve HIV-related communication in a limited-resource setting: a short report.

Infographics (visualizations that present information) can assist clinicians to offer health information to patients with low health literacy in an accessible format. In response, we developed an infographic intervention to enhance clinical, HIV-related communication. This study reports on its feasibility and acceptability at a clinical setting in the Dominican Republic. We conducted in-depth interviews with physicians who administered the intervention and patients who received it. We conducted audio-recorded interviews in Spanish using semi-structured interview guides. Recordings were professionally transcribed verbatim then analyzed using descriptive content analysis. Physician transcripts were deductively coded according to constructs of Bowen et al.'s feasibility framework and patient transcripts were inductively coded. Three physicians and 26 patients participated. Feasibility constructs endorsed by physicians indicated that infographics were easy to use, improved teaching, and could easily be incorporated into their workflow. Coding of patient transcripts identified four categories that indicated the intervention was acceptable and useful, offered feedback regarding effective clinical communication, and recommended improvements to infographics. Taken together, these data indicate our intervention was a feasible and acceptable way to provide clinical, HIV-related information and provide important recommendations for future visualization design as well as effective clinical communication with similar patient populations.

Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments.

In the information age, the ability to read and construct data visualizations becomes as important as the ability to read and write text. However, while standard definitions and theoretical frameworks to teach and assess textual, mathematical, and visual literacy exist, current data visualization literacy (DVL) definitions and frameworks are not comprehensive enough to guide the design of DVL teaching and assessment. This paper introduces a data visualization literacy framework (DVL-FW) that was specifically developed to define, teach, and assess DVL. The holistic DVL-FW promotes both the reading and construction of data visualizations, a pairing analogous to that of both reading and writing in textual literacy and understanding and applying in mathematical literacy. Specifically, the DVL-FW defines a hierarchical typology of core concepts and details the process steps that are required to extract insights from data. Advancing the state of the art, the DVL-FW interlinks theoretical and procedural knowledge and showcases how both can be combined to design curricula and assessment measures for DVL. Earlier versions of the DVL-FW have been used to teach DVL to more than 8,500 residential and online students, and results from this effort have helped revise and validate the DVL-FW presented here.

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review.

BACKGROUND: Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. OBJECTIVE: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. METHODS: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. RESULTS: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. CONCLUSIONS: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.

Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as "black boxes". Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.

CiteSpace-Based Visualization Analysis of Forensic Research in China from 2010 to 2019.

OBJECTIVES: To analyze the research status of forensic medicine in China from 2010 to 2019, obtain the development trend of forensic medicine and explore the hotspots and research frontiers. METHODS: The forensic medical academic papers published on China National Knowledge Infrastructure (CNKI) database from 2010 to 2019 were collected. CiteSpace 5.7.R1, an information visualization analysis software, was used to analyze publication organizations, authors, keywords, and other elements. RESULTS: The majority of the research institutions were universities, provincial and ministerial scientific research and forensic institutions. Forensic pathology was still an important branch of forensic medicine and a popular research direction. The "polymorphism" and "Y chromosome" had been the research hotspots in recent years. "Medical damage" and "standard" were the most novel studies. CONCLUSIONS: In order to provide scientific basis and research direction for forensic research, this paper analyzes the cooperation network, research hotspots and research innovation in forensic research.

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species.

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.

Clinician Use of HIV-Related Infographics During Clinic Visits in the Dominican Republic is Associated with Lower Viral Load and Other Improvements in Health Outcomes.

We designed an infographic intervention to help clinicians provide health information to persons living with HIV. In this study, we assessed the extent to which our intervention may improve objectively and subjectively measured health outcomes (CD4 count, viral load, and engagement with clinician among others) when integrated into routine visits in the Dominican Republic. In this pretest-posttest study, we followed participants for 9 months at 3-month intervals. Physicians administered the intervention during participants' first 3 visits. Outcome measures, selected using a conceptual model, were assessed at 4 time points. We assessed changes in outcomes over time with general linear regressions and Wilcoxon Signed-Rank tests. Participants (N = 50) were mostly female (56%) and had been living with HIV for a mean of 6.3 years (SD = 6.1). All outcomes, except CD4 count, demonstrated statistically significant improvements by study end. This provides preliminary evidence our intervention may improve outcomes, but further testing is needed.

RESUMEN: Diseñamos una intervención infográfica para ayudar a los médicos brindar información médica a personas viviendo con el VIH. En este estudio, evaluamos en qué medida nuestra intervención puede mejorar los resultados de salud (conteo de CD4, carga viral, y compromiso con el médico entre otros), medidos de una manera objetiva y subjetiva, cuando se incorpora en las visitas médicas de rutina en la República Dominicana. En este estudio de prueba previo y posterior, seguimos los participantes durante 9 meses a intervalos de 3 meses. Los médicos administraron la intervención durante las primeras 3 visitas de los participantes. Seleccionamos las medidas de resultado utilizando un marco conceptual y las evaluamos en los 4 puntos de tiempo. Evaluamos cambios a lo largo del tiempo usando regresiones lineales generales y pruebas de asociación de Wilcoxon Signed-Rank. Los participantes (N = 50) fueron mayormente mujeres (56%) y habían estado viviendo con el VIH durante una media de 6,3 años (DE = 6,1). Todos los resultados, aparte del conteo de CD4, demostraron mejoras estadísticamente significativas al final del estudio. Esto proporciona evidencia preliminar de que nuestra intervención puede mejorar los resultados de la salud, pero se justifican pruebas adicionales.

A Hybrid Approach to Industrial Augmented Reality Using Deep Learning-Based Facility Segmentation and Depth Prediction.

Typical AR methods have generic problems such as visual mismatching, incorrect occlusions, and limited augmentation due to the inability to estimate depth from AR images and attaching the AR markers onto physical objects, which prevents the industrial worker from conducting manufacturing tasks effectively. This paper proposes a hybrid approach to industrial AR for complementing existing AR methods using deep learning-based facility segmentation and depth prediction without AR markers and a depth camera. First, the outlines of physical objects are extracted by applying a deep learning-based instance segmentation method to the RGB image acquired from the AR camera. Simultaneously, a depth prediction method is applied to the AR image to estimate the depth map as a 3D point cloud for the detected object. Based on the segmented 3D point cloud data, 3D spatial relationships among the physical objects are calculated, which can assist in solving the visual mismatch and occlusion problems properly. In addition, it can deal with a dynamically operating or a moving facility, such as a robot-the conventional AR cannot do so. For these reasons, the proposed approach can be utilized as a hybrid or complementing function to existing AR methods, since it can be activated whenever the industrial worker requires handing of visual mismatches or occlusions. Quantitative and qualitative analyses verify the advantage of the proposed approach compared with existing AR methods. Some case studies also prove that the proposed method can be applied not only to manufacturing but also to other fields. These studies confirm the scalability, effectiveness, and originality of this proposed approach.

Fractional-Order Sensing and Control: Embedding the Nonlinear Dynamics of Robot Manipulators into the Multidimensional Scaling Method.

This paper studies the use of multidimensional scaling (MDS) to assess the performance of fractional-order variable structure controllers (VSCs). The test bed consisted of a revolute planar robotic manipulator. The fractional derivatives required by the VSC can be obtained either by adopting numerical real-time signal processing or by using adequate sensors exhibiting fractional dynamics. Integer (fractional) VCS and fractional (integer) sliding mode combinations with different design parameters were tested. Two performance indices based in the time and frequency domains were adopted to compare the system states. The MDS generated the loci of objects corresponding to the tested cases, and the patterns were interpreted as signatures of the system behavior. Numerical experiments illustrated the feasibility and effectiveness of the approach for assessing and visualizing VSC systems.

[Visual analysis of knowledge map of network Meta-analysis in traditional Chinese medicine based on CiteSpace].

Network Meta-analysis has been widely applied in the field of traditional Chinese medicine(TCM) due to its unique advantages. This study aimed to conduct a visual analysis on the state of the application network Meta-analysis in the field of traditional Chinese medicine. Databases of CNKI and Web of Science were retrieved to identify the qualified literatures and then screen out their titles and abstracts. Institutions, authors, cited references, and keywords were analyzed using the information visualization analysis software CiteSpace. Finally, 79 English and 186 Chinese articles were included. The results indicated that the literatures were mainly published in Chinese, and the number of articles was increased rapidly since 2015. Cooperation between institutions and authors were mainly concentrated inside the institutions. The most important four institutions were four universities who attached more importance to evidence-based medical education and academic exploration. The keywords beside the method of network Meta-analysis could be summarized into three types: the main interventions in traditional Chinese medicine(Chinese herbal injection, herb medicine, acupuncture, etc.), disease types(cancer, circulatory system disease, bone joint disease, urinary system disease, etc.) and the outcome of interests(efficacy, safety, symptom, survive, mortality, etc.), which reflected the current research hotspots to certain extent. In addition, the most cited articles were methodology articles, including the introduction of methodology and the guides of application software, suggesting that the exploration of methodological articles will be extremely concerned.

A semi-automated approach for analyzing collages to inform the design of a family health information management system for Hispanic dementia caregivers.

Culturally- and linguistically-tailored health communication is needed for vulnerable populations to manage their health and the health of their families. This presents a significant design challenge. The use of collages is an increasingly popular technique with the flexibility to capture the needs and experiences of individuals with various cultural and language preferences. Collage analysis has typically remained qualitative in nature. We introduce a novel, objective, semi-automated approach that enhances collage analysis to elucidate pattern differences that may not be detectable by natural perception. We present a case scenario of collage analysis based on the expressed experience and self-management needs of Hispanic dementia caregivers (n = 24). We demonstrate how our innovative approach may reveal cultural differences between language groups that could have otherwise been missed using traditional techniques.

The Improvement of the Electronic Health Record User Experience by Screen Design Principles.

The amount and complexity of data accumulated in electronic health record (EHR) is quite large, and the quantity of information that clinicians can examine and handle is very limited. Thus, it is necessary to enhance the accessibility of EHR by improving the user experience (UX). To apply information visualization that turns EHR data into an understandable visual format, we propose following five screen design principles when designing UX interfaces. #1: One view should contain single patient data. #2: Data should be summarized or titled for overview and details should be given on-demand. #3: Data should be displayed in time-series. #4: Data should be categorized by primary type. #5: More data should be displayed at the same time. Three screen designs are plausible utilizing the above-mentioned principles. To measure the UX of screen designs and validate the design principles, we built an EHR viewer system that has three windows corresponding to these screen designs and had it tested by medical staff. The results of the test revealed that the UX of the screen design is proportional to the number of design principles that the screen design incorporates. It shows that the proposed screen design principles improve the UX of EHR.

Timeline representation of clinical data: usability and added value for pharmacovigilance.

BACKGROUND: Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions (ADR). This activity requires the collection and analysis of data from the patient record or any other sources to find clues of a causality link between the drug and the ADR. This can be time-consuming because often patient data are heterogeneous and scattered in several files. To facilitate this task, we developed a timeline prototype to gather and classify patient data according to their chronology. Here, we evaluated its usability and quantified its contribution to routine pharmacovigilance using real ADR cases. METHODS: The timeline prototype was assessed using the biomedical data warehouse eHOP (from entrepôt de données biomédicales de l'HOPital) of the Rennes University Hospital Centre. First, the prototype usability was tested by six experts of the Regional Pharmacovigilance Centre of Rennes. Their experience was assessed with the MORAE software and a System and Usability Scale (SUS) questionnaire. Then, to quantify the timeline contribution to pharmacovigilance routine practice, three of them were asked to investigate possible ADR cases with the "Usual method" (analysis of electronic health record data with the DxCare software) or the "Timeline method". The time to complete the task and the data quality in their reports (using the vigiGrade Completeness score) were recorded and compared between methods. RESULTS: All participants completed their tasks. The usability could be considered almost excellent with an average SUS score of 82.5/100. The time to complete the assessment was comparable between methods (P = 0.38) as well as the average vigiGrade Completeness of the data collected with the two methods (P = 0.49). CONCLUSIONS: The results showed a good general level of usability for the timeline prototype. Conversely, no difference in terms of the time spent on each ADR case and data quality was found compared with the usual method. However, this absence of difference between the timeline and the usual tools that have been in use for several years suggests a potential use in pharmacovigilance especially because the testers asked to continue using the timeline after the evaluation.

An Information-Theoretic Framework for Evaluating Edge Bundling Visualization.

Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented.

Using visual analytics for presenting comparative information on new drugs.

OBJECTIVE: When a new drug is marketed, physicians must decide whether they will consider it for their future practice. However, information about new drugs can be biased or hard to find. In this work, our objective was to study whether visual analytics could be used for comparing drug properties such as contraindications and adverse effects, and whether this visual comparison can help physicians to forge their own well-founded opinions about a new drug. MATERIALS AND METHODS: First, an ontology for comparative drug information was designed, based on the expectations expressed during focus groups comprised of physicians. Second, a prototype of a visual drug comparator website was developed. It implements several visualization methods: rainbow boxes (a new technique for overlapping set visualization), dynamic tables, bar charts and icons. Third, the website was evaluated by 22 GPs for four new drugs. We recorded the general satisfaction, the physician's decision whether to consider the new drug for future prescription, both before and after consulting the website, and their arguments to justify their choice. RESULTS: The prototype website permits the visual comparison of up to 10 drugs, including efficacy, contraindications, interactions, adverse effects, prices, dosage regimens,…All physicians found that the website allowed them to forge a well-founded opinion on the four new drugs. The physicians changed their decision about using a new drug in their future practice in 29 cases (out of 88) after consulting the website. DISCUSSION AND CONCLUSION: Visual analytics is a promising approach for presenting drug information and for comparing drugs. The visual comparison of drug properties allows physicians to forge their opinions on drugs. Since drug properties are available in reference texts, reviewed by public health agencies, it could contribute to the independent of drug information.

Evolutionary Visual Exploration: Evaluation of an IEC Framework for Guided Visual Search.

We evaluate and analyse a framework for evolutionary visual exploration (EVE) that guides users in exploring large search spaces. EVE uses an interactive evolutionary algorithm to steer the exploration of multidimensional data sets toward two-dimensional projections that are interesting to the analyst. Our method smoothly combines automatically calculated metrics and user input in order to propose pertinent views to the user. In this article, we revisit this framework and a prototype application that was developed as a demonstrator, and summarise our previous study with domain experts and its main findings. We then report on results from a new user study with a clearly predefined task, which examines how users leverage the system and how the system evolves to match their needs. While we previously showed that using EVE, domain experts were able to formulate interesting hypotheses and reach new insights when exploring freely, our new findings indicate that users, guided by the interactive evolutionary algorithm, are able to converge quickly to an interesting view of their data when a clear task is specified. We provide a detailed analysis of how users interact with an evolutionary algorithm and how the system responds to their exploration strategies and evaluation patterns. Our work aims at building a bridge between the domains of visual analytics and interactive evolution. The benefits are numerous, in particular for evaluating interactive evolutionary computation (IEC) techniques based on user study methodologies.

Open-Source, Web-Based Dashboard Components for DICOM Connectivity.

The administration of a DICOM network within an imaging healthcare institution requires tools that allow for monitoring of connectivity and availability for adequate uptime measurements and help guide technology management strategies. We present the implementation of an open-source widget for the Dashing framework that provides basic dashboard functionality allowing for monitoring of a DICOM network using network "ping" and DICOM "C-ECHO" operations.