Search

The increasing use of patient-reported outcome (PRO) measures is forcing clinicians and health care systems to decide which to select and how to incorporate them into their records and clinical workflows. This overview addresses 3 topics related to these concerns. First, a literature review summarizes key psychometric and practical factors (such as reliability, responsiveness, computer adaptive testing, and interpretability) in choosing PROs for clinical practice. Second, 3 clinical decision support issues are highlighted: gathering PROs, electronic health record effect on providers, and incorporating PROs into clinical decision support design and implementation. Lastly, the salience of crosscutting domains as well as 9 key pragmatic decisions are reviewed. Crosscutting domains are those that are relevant across most medical and mental health conditions, such as the SPADE symptom pentad (sleep problems, pain, anxiety, depression, low energy/fatigue) and physical functioning. The 9 pragmatic decisions include (1) generic vs disease-specific scales; (2) single- vs multidomain scales; (3) universal scales vs user-choice selection; (4) number of domains to measure; (5) prioritization of domains when multiple domains are assessed; (6) action thresholds; (7) clinical purpose (screening vs monitoring); as well as the (8) frequency and (9) logistical aspects of PRO administration.

Objective To identify the appropriate treatment in the tonsillar region's infections, a distinction between tonsillitis with or without abscess formation is essential. Ultrasound proved to be a valuable method in identifying abscess formation in the head and neck region. However, no report described the assessment of the tonsil region. This study aims to determine the feasibility of transcervical ultrasound for abscess visualization in the palatal tonsillar region. Methods Retrospective analysis of 354 patients presenting with clinical suspicion of tonsillar abscess to a tertiary referral center and university hospital. All patients received a transcervical ultrasound to establish a primary diagnosis. The existence of an abscess was confirmed by puncture and incision, or final tonsillectomy. If no abscess could be delineated, non-abscessing tonsillitis was supposed, and conservative primary therapy and closed follow-up examinations were performed. Results After the first diagnostic ultrasound examination, in 257 cases (72.59%), the diagnosis of an abscess could be established, but in 97 cases (27.40%), due to missing abscess formation criteria, non-abscessing tonsillitis was documented. Overall, ultrasonography demonstrated an overall accuracy of 78.8% in this selected cohort. The sensitivity, specificity, PPV, and NPV after the first ultrasound examination were calculated with 75.1%, 88.6%, 94.6%, and 57.3%, respectively. Conclusion The presented data confirm that an abscess formation due to tonsillitis can be detected by transcervical ultrasound, enabling prompt, adequate management. As transcervical ultrasound can be conducted fast and is not associated with radiation, it can be regarded as a first-line diagnostic tool in this condition.

Background For Hepatocellular carcinoma (HCC) surgery either through resection or transplantation often provides the only chance for cure. Since hepatocarcinogenesis and postsurgical prognosis is not only dependent on cirrhosis but also on immune activation and exhaustion, many studies have investigated tumor infiltrating leukocyte (TIL) subsets. This systematic review and meta-analysis aims at describing the cell groups and their predictive power regarding overall (OS), disease free (DFS) and recurrence free survival (RFS). Material and methods A systematic search of the PubMed database was conducted (PROSPERO 172324). Data on CD3+, CD8+, Treg, B cells, macrophages, neutrophil and NK-cells were collected from Pubmed and related references up to December 2018. Overall (OS), disease-free (DFS) and recurrence free survival (RFS) in dependence of high vs. low infiltration rates were compared using a random effects meta-analysis. Results Altogether data from 3541 patients enrolled in 20 publications were included. Except for Tregs and Neutrophils, heterogeneity analysis was found to be moderate to high across the studies. High CD3+, CD8+, NK-cell infiltration predicted better survival (OS, DFS and RFS; p Discussion As with other solid tumors immune infiltration has a great influence on survival after resection. However, a considerable publication bias cannot be ruled out in mostly retrospective analyses. Nevertheless, in light of novel immune modulatory treatments this opens a new avenue towards effective and well-tolerated adjuvant treatment.

BACKGROUND Pancreatic panniculitis is an extremely rare condition associated with different underlying pancreatic disorders and characterized by subcutaneous fat necrosis induced by elevated serum lipase levels. These lesions usually affect the lower extremities and may precede abdominal symptoms of pancreatic disease. Acinar cell carcinoma (ACC) of the pancreas is a rare pancreatic neoplasm, accounting for only 1%-2% of pancreatic tumors in adults. CASE SUMMARY We present the case of a 72-year-old man with ACC of the pancreatic head and synchronous liver metastases. Both the primary tumor and liver metastases were resected. Serum lipase was elevated before surgery and decreased to normal postoperatively. Rising serum lipase levels at follow-up led to the diagnosis of hepatic recurrence. This disease progression was then accompanied by pancreatic panniculitis, with subcutaneous fat necrosis and acute arthritis. To the best of our knowledge, only 4 cases have been reported in the literature and each showed a similar association of serum lipase levels with pancreatic panniculitis and progression of ACC. CONCLUSION Clinical symptoms and progression of ACC may correlate with serum lipase levels, suggesting potential usefulness as a follow-up biomarker.

Surgical resection improves survival in pancreatic ductal adenocarcinoma (PDAC) and adjuvant chemotherapy adds an additional survival-benefit. While surgical technique has improved in recent years, it remains unclear whether these improvements translate into a survival benefit independent of adjuvant chemotherapy. Thus, we aimed to clarify whether survival of patients who were treated with either Gemcitabine (GEM) or who were observed only in randomized controlled trials on adjuvant chemotherapy of PDAC improved over time.A systematic search of MEDLINE/PubMed was performed to identify randomized controlled trials on adjuvant chemotherapy of PDAC. The search was limited to studies with arms on GEM monotherapy or postoperative observation and studies were grouped by the median year of enrolment and the use of GEM. Subsequently, a meta-regression on the effect of the median year of enrolment on patient survival was performed.A total of 13 studies with 2469 patients was included, with median years of enrollment ranging from 1996 to 2015. While disease-free survival decreased in patients with postoperative observation (18.0 vs. 5.0 months, p = 0.001), median survival improved over time in patients with postoperative observation (15.8 vs. 18.4 months, p = 0.01) and in patients treated with adjuvant GEM (22.8 vs. 35.0 months, p 0.001). One- (p ≤ 0.01) and two-year survival (p = 0.056) improved in both patients treated with adjuvant GEM and those observed only.Survival after surgical resection of PDAC has improved since 1996, even in patients who did not receive adjuvant chemotherapy. Improved surgical technique and postoperative management are likely to be causative factors.

Trust‐ability, reputation, security and quality are the main concerns for public and private financial institutions. To detect fraudulent behaviour, several techniques are applied pursuing different goals. For well‐defined problems, analytical methods are applicable to examine the history of customer transactions. However, fraudulent behaviour is constantly changing, which results in ill‐defined problems. Furthermore, analysing the behaviour of individual customers is not sufficient to detect more complex structures such as networks of fraudulent actors. We propose NEVA (Network dEtection with Visual Analytics), a Visual Analytics exploration environment to support the analysis of customer networks in order to reduce false‐negative and false‐positive alarms of frauds. Multiple coordinated views allow for exploring complex relations and dependencies of the data. A guidance‐enriched component for network pattern generation, detection and filtering support exploring and analysing the relationships of nodes on different levels of complexity. In six expert interviews, we illustrate the applicability and usability of NEVA.

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk‐through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.

Cultural object collections attract and delight spectators since ancient times. Yet, they also easily overwhelm visitors due to their perceptual richness and associated information. Similarly, digitized collections appear as complex, multifaceted phenomena, which can be challenging to grasp and navigate. Though visualizations can create various types of collection overviews for that matter, they do not easily assemble into a "big picture" or lead to an integrated understanding. We introduce coherence techniques to maximize connections between multiple views and apply them to the prototype PolyCube system of collection visualization: with map, set, and network visualizations it makes spatial, categorical, and relational collection aspects visible. For the essential temporal dimension, it offers four different views: superimposition, animation, juxtaposition, and space-time cube representations. A user study confirmed that better integrated visualizations support synoptic, cross-dimensional insights. An outlook is dedicated to the system's applicability within other arts and humanities data domains.

Purpose 68Ga-PSMA-11-PET/CT is increasingly used in early-stage biochemical recurrence of prostate cancer to detect potential lesions for an individualized radiotherapy concept. However, subtle findings especially concerning small local recurrences can still be challenging to interpret and are prone to variability between different readers. Thus, we analyzed interobserver variability, detection rate, and lesion patterns systematically in a homogeneous patient population with low-level biochemical recurrence. Methods We analyzed 68Ga-PSMA-11-PET/CTs in 116 patients with status post-prostatectomy and PSA levels up to 0.6 ng/ml. None of them received ADT or radiotherapy beforehand. Images were interpreted and blinded by two nuclear medicine physicians (R1 and R2). Findings were rated using a 5-point scale concerning local recurrence, lymph nodes, bone lesions, and other findings (1: definitely benign, 2: probably benign, 3: equivocal, 4: probably malignant, 5: definitely malignant). In findings with substantial discrepancies of 2 or more categories and/or potentially leading to differences in further patient management, a consensus reading was done with a third reader (R3). Interobserver agreement was measured by Cohens Kappa analysis after sub-categorizing our classification system to benign (1 + 2), equivocal (3), and malignant (4 + 5). Time course of PSA levels after salvage treatment of patients rated as positive (4 + 5) was analyzed. Results The overall detection rate (categories 4 and 5) was 50% (R1/R2, 49%/51%) and in the PSA subgroups 0–0.2 ng/ml, 0.21–0.3 ng/ml, and 0.31–0.6 ng/ml 24%/27%, 57%/57%, and 65%/68%, respectively. Local recurrence was the most common lesion manifestation followed by lymphatic and bone metastases. The overall agreement in the Cohens Kappa analysis was 0.74 between R1 and R2. For local, lymphatic, and bone sites, the agreement was 0.76, 0.73, and 0.58, respectively. PSA levels of PSMA PET/CT-positive patients after salvage treatment decreased in 75% (27/36) and increased in 25% (9/36). A decrease of PSA, although more frequent in patients with imaging suggesting only local tumor recurrence (86%, 18/21), was also observed in 67% (10/15) of patients with findings of metastatic disease. Conclusions In a highly homogeneous group of prostate cancer patients with early-stage biochemical recurrence after radical prostatectomy, we could show that 68Ga-PSMA-11-PET/CT has a good detection rate of 50% which is in accordance with literature, with clinically relevant findings even in patients with PSA

Chronic pancreatitis (CP) is associated with alcohol abuse in 80% of cases. The primary treatment goals in CP are pain reduction and avoidance of pancreatitis-associated complications. CP should be treated in an interdisciplinary approach. A recent randomized clinical trial showed that early surgery compared with an endoscopy-first approach resulted in reduced pain levels. Surgical resections are, therefore, the most efficient treatment of pancreatitis-associated pain as well as other complications and should be performed early in the course of the disease. Since most of the patients pre­sent with chronic inflammation of the pancreatic head, pancreatic head resection is the most common treatment option. Duodenum-preserving pancreatic head resections are the surgical procedure of choice, but pancreaticoduodenectomies (Kausch-Whipple procedures) demonstrate similar outcome with regard to pain control, quality of life, and metabolic parameters. Other surgical procedures, including drainage procedures, pancreatic segmental resections, or left resections, are rarely indicated.

The phenomenon of a blockchain use case called initial coin offering (ICO) is drawing increasing attention as a novel funding mechanism. ICO is a crowdfunding type that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although more than $7bn has been raised globally via ICOs as at 2018, the concept and its implications are not yet entirely understood. The research lags behind in providing in-depth analyses of ICO designs and their long-term success. We address this research gap by developing an ICO taxonomy, applying a cluster analysis to identify prevailing ICO archetypes, and providing an outlook on the token value market performance for individual archetypes. We identify five ICO design archetypes and display their secondary market development from both a short-term and a long-term perspective. We contribute to an in-depth understanding of ICOs and their implications. Further, we offer practitioners tangible design and success indications for future ICOs.

Providing guidance during a Visual Analytics session can support analysts in pursuing their goals more efficiently. However, the effectiveness of guidance depends on many factors: Determining the right timing to provide it is one of them. Although in complex analysis scenarios choosing the right timing could make the difference between a dependable and a superfluous guidance, an analysis of the literature suggests that this problem did not receive enough attention. In this paper, we describe a methodology to determine moments in which guidance is needed. Our assumption is that the need of guidance would influence the user state-of-mind, as in distress situations during the analytical process, and we hypothesize that such moments could be identified by analyzing the user's facial expressions. We propose a framework composed by a facial recognition software and a machine learning model trained to detect when to provide guidance according to changes of the user facial expressions. We trained the model by interviewing several analysts during their work and ranked multiple facial features based on their relative importance in determining the need of guidance. Finally, we show that by applying only minor modifications to its architecture, our prototype was able to detect a need of guidance on the fly and made our methodology well suited also for real-time analysis sessions. The results of our evaluations show that our methodology is indeed effective in determining when a need of guidance is present, which constitutes a prerequisite to providing timely and effective guidance in VA.

Primary care physicians (general practitioners (GPs)) are burdened for various reasons and are particularly affected by stress-related complaints and an increasing prevalence of burnout. Thus, the prevention of physician burnout has become a major interest for health care services. Although many studies have addressed this issue in recent years, little seems to be known about the work strain and burnout rates in GP trainees. Therefore the objective of this article is to review the psychosocial burden and relevant prevention strategies for GPs with a special emphasis on GP trainees. Regardless of the specialty, burnout is more prevalent among medical trainees and so-called 'early career' physicians than among the age-matched population. Accordingly, burnout seems to be frequent among GP trainees, although there is some evidence that there are fewer doctors working in general medicine who were already heavily burdened at the time of choosing their career. The sudden assumption of responsibility in patient care as well as the fear of showing imperfection in front of their supervisors, or lack of recognition from senior doctors, the medical team, or patients might be stressors typical to this career stage. GP trainees might also feel burdened by the new level of personal involvement and thus have to develop or increase their individual level of professionality to deal with the patients' medical and personal problems. In conclusion, interventions to promote physical and mental health of GP trainees are a necessity to ensure passionate GPs in the future and should therefore be integrated into any postgraduate training curriculum in general practice.

Objective To systematically examine clinical workflows before and after a major electronic health record (EHR) implementation, we performed this study. EHR implementation and/or conversion are associated with many challenges, which are barriers to optimal care. Clinical workflows may be significantly affected by EHR implementations and conversions, resulting in provider frustration and reduced efficiency. Patients and Methods Our institution completed a large EHR conversion and workflow standardization converting from 3 EHRs (GE Centricity and 2 versions of Cerner) to a system-wide Epic platform. To study this quantitatively and qualitatively, we collected and curated clinical workflows through rapid ethnography, workflow observation, video ethnography, and log-file analyses of hundreds of providers, patients, and more than 100,000 log files. The study included 5 geographic sites in 4 states (Arizona, Minnesota, Florida, and Wisconsin). This project began in April 2016, and will be completed by December 2019. Our study began on May 1, 2016, and is ongoing. Results Salient themes include the importance of prioritizing clinical areas with the most intensive EHR use, the value of tools to identify bottlenecks in workflow that cause delays, and desire for additional training to optimize navigation. Video microanalyses identified marked differences in patterns of workflow and EHR navigation patterns across sites. Log-file analyses and social network analyses identified differences in personnel roles, which led to differences in patient–clinician interaction, time spent using the EHR, and paper-based artifacts. Conclusion Assessing and curating workflow data before and after EHR conversion may provide opportunities for unexpected efficiencies in workflow optimization and information-system redesign. This project may be a model for capturing significant new knowledge in using EHRs to improve patient care, workflow efficiency, and outcomes.

After decades of digitization, large cultural heritage collections have emerged on the web, which contain massive stocks of content from galleries, libraries, archives, and museums. This increase in digital cultural heritage data promises new modes of analysis and increased levels of access for academic scholars and casual users alike. Going beyond the standard representations of search-centric and grid-based interfaces, a multitude of approaches has recently started to enable visual access to cultural collections, and to explore them as complex and comprehensive information spaces by the means of interactive visualizations. In contrast to conventional web interfaces, we witness a widening spectrum of innovative visualization types specially designed for rich collections from the cultural heritage sector. This new class of information visualizations gives rise to a notable diversity of interaction and representation techniques while lending currency and urgency to a discussion about principles such as serendipity, generosity, and criticality in connection with visualization design. With this survey, we review information visualization approaches to digital cultural heritage collections and reflect on the state of the art in techniques and design choices. We contextualize our survey with humanist perspectives on the field and point out opportunities for future research.

Ceramic fibers are just as glass and basalt member of the group of inorganic fibers. Like most types of inorganic fibers ceramic fibers have a high tear resistance but a limited flexibility. [1] Ceramic fibers are characterized by their extraordinary high temperature and chemical resistance. These properties make them interesting for different high technical applications, as they occur in aerospace, chemical-and energy technology. In this field, they are applied especially as a reinforcement component in composite materials. Not only the partially high material price, but although the typical brittleness of ceramic fibers bring huge problems during the textile production chain, which limits the availability of complex textile preforms in the market. Often, a radical revision of the machine and processing concept is necessary to enable an economical production process. The Application Center for Textile Fiber Ceramics TFK at Fraunhofer-Center for High Temperature Materials and Design HTL develops and modifies textile production processes to make them suitable for the special requirements of ceramic fibers. One and multilayer woven fabrics, braids and tape structures for the winding process have already been successfully implemented. A further development complex is the intensive investigation of three-dimensional textile reinforcement structures. Regarding the high material costs, these research activities are very important. If the textile reinforcement is placed only where needed, the amount of used fiber material can be reduced significantly.

A temporal graph stores and reflects temporal information associated with its entities and relationships. Such graphs can be utilized to model a broad variety of problems in a multitude of domains. Re-searchers from different fields of expertise are increasingly applying graph visualization and analysis to explore unknown phenomena, complex emerging structures, and changes occurring over time in their data. While several empirical studies evaluate the benefits and drawbacks of different network representations, visualizing the temporal dimension in graphs still presents an open challenge. In this paper we propose an exploratory user study with the aim of evaluating different combinations of graph representations, namely node-link and adjacency matrix, and temporal encodings, such as superimposition, juxtaposition and animation, on typical temporal tasks. The study participants expressed positive feedback toward matrix representations, with generally quicker and more accurate responses than with the node-link representation.

Caused by a newly discovered coronavirus, COVID-19 is an infectious disease easily transmitted between people through close contacts that had exponential global growth in 2020 and became, in a very short time, a major health, and economic global issue. Real-world data concerning the spread of the disease was quickly made available by different global institutions and resulted in many works involving data visualizations and prediction models. In this paper, (1) we discuss the problem, data aspects, and challenges of COVID-19 data analysis; (2) We propose a Visual Analytics approach (called COVis) combining different temporal aspects of COVID-19 data with the output of a predictive model. This combination supports the estimation of the spread of the disease in different scenarios and allows correlating and monitoring the virus development in relation to different government response events; (3) We evaluate the approach with two domain experts to support the understanding of how our system can facilitate journalistic investigation tasks and (4) we discuss future works and a possible generalization of our solution.

Background: Previously we developed a structured case management for patients with multiple chronic conditions in primary care (called ‘PraCMan’), delivered by Health Care Assistants in General Practice (VERAHs). The ‘TelePraCMan’ project aims to develop a supplementary web-based application to ‘PraCMan’, including a digital symptom diary in which patients can enter individual data via smartphone/tablet. This application is targeted at patients suffering from diabetes mellitus type 2, COPD, hypertension and/or chronic heart failure. Objective: The objective of this study is to evaluate this mHealth-intervention for patients with chronic diseases, focused on patients’ quality of life and self-management capabilities.Methods: A two-armed patient-randomized pilot trial is planned, involving 200 multimorbid patients in 20 GP-practices that use ‘PraCMan’ regularly. Participants are randomly assigned (1:1) to receive either standard ‘PracMan’ treatment or standard ‘PracMan’ treatment complemented with the mHealth-intervention (TelePraCMan). As primary outcome, the health-related quality of life is measured by the German version of the Short Form Health Questionnaire (SF-12). Secondary outcomes include self-management capabilities, technical affinity, and user experience of patient and practice staff. Outcomes are measured at baseline (T0) and at follow-up after 6 months (T1). For the evaluation of the implementation process, GPs, VERAHs and patients will be invited to participate in semi structured interviews afterwards.The outcomes will be compared between study arms using multilevel regression analysis. Qualitative interviews will be transcribed and analysed using qualitative content analysis based on the Tailored Implementation for Chronic Diseases Framework. Discussion: ‘TelePraCMan’ is one of the first mHealth-Interventions addressing multimorbid patients in primary care.Trial registration: DRKS, DRKS00017320, Registered 18 June 2019, http://www.drks.de/DRKS00017320

Clinician task performance is significantly impacted by the navigational efficiency of the system interface. Here we propose and evaluate a navigational complexity framework useful for examining differences in electronic health record (EHR) interface systems and their impact on task performance. The methodological approach includes 1) expert-based methods—specifically, representational analysis (focused on interface elements), keystroke level modeling (KLM), and cognitive walkthrough; and 2) quantitative analysis of interactive behaviors based on video-captured observations. Medication administration record (MAR) tasks completed by nurses during preoperative (PreOp) patient assessment were studied across three Mayo Clinic regional campuses and three different EHR systems. By analyzing the steps executed within the interfaces involved to complete the MAR tasks, we characterized complexities in EHR navigation. These complexities were reflected in time spent on task, click counts, and screen transitions, and were found to potentially influence nurses’ performance. Two of the EHR systems, employing a single screen format, required less time to complete (mean 101.5, range 106–97 s), respectively, compared to one system employing multiple screens (176 s, 73% increase). These complexities surfaced through trade-offs in cognitive processes that could potentially influence nurses’ performance. Factors such as perceptual-motor activity, visual search, and memory load impacted navigational complexity. An implication of this work is that small tractable changes in interface design can substantially improve EHR navigation, overall usability, and workflow.

In social networks, individuals' decisions are strongly influenced by recommendations from their friends and acquaintances. The influence maximization (IM) problem asks to select a seed set of users that maximizes the influence spread, i.e., the expected number of users influenced through a stochastic diffusion process triggered by the seeds. In this paper, we present VAIM, a visual analytics system that supports users in analyzing the information diffusion process determined by different IM algorithms. By using VAIM one can: (i) simulate the information spread for a given seed set on a large network, (ii) analyze and compare the effectiveness of different seed sets, and (iii) modify the seed sets to improve the corresponding influence spread., Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)

Background Reporting of adverse events is an important aspect of patient safety management in hospitals, which may help to prevent future adverse events. Yet, only a small proportion of such events is actually reported in German hospitals. Therefore, it is crucial to evaluate attitudes of clinical staff towards reporting of adverse events. The aim of this study was to translate the Reporting of Clinical Adverse Events Scale (RoCAES) developed by Wilson, Bekker and Fylan (2008) and validate it in a sample of German-speaking health professionals. Methods The questionnaire covers five factors (perceived blame, perceived criteria for identifying events that should be reported, perceptions of colleagues’ expectations, perceived benefits of reporting, and perceived clarity of reporting procedures) and was translated into German language according to translation guidelines. Within a cross-sectional study in a sample of 120 health professionals in German hospitals, internal consistency (omega) and construct validity (confirmatory factor analysis) of the German scale RoCAES-D was assessed. Results The reliability was high (omega = 0.87) and the factor analysis showed a poor model fit (RMSEA: 0.074, χ2/df: 1.663, TLI: 0.690). Resulting from lower model fit of the original model (RMSEA: 0.082, χ2/df: 1.804, TLI: 0.606), one item was deleted due to low factor loadings and a low R2 (0.001), and two items were reallocated from the factor ‘perceived benefits’ to ‘perceived blame’. Conclusion The successful translation and initial validation of the RoCAES-D might be a good starting point for further research. A cultural adaptation of the scale needs to be done to initiate a large-scale usage of the questionnaire.

Visualization envisions to intertwine the strengths of humans and computers for effective interactive visual and analytic data analysis and exploration. To this end, humans’ tacit/implicit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the visual and analytic exploration processes. However, acquiring, structuring, formalizing, storing, and utilizing implicit and explicit knowledge within the whole visualization process are provocative and widely-discussed research challenge. This chapter elaborates on (1) knowledge-assisted visualization, which aims to incorporate implicit and explicit knowledge as well as information-theoretical considerations into the visualization process to support users for decision making and (2) guidance, which is a computer-assisted process that aims to actively resolve a knowledge gap encountered by users during an interactive visualization session. This chapter ends with critical reflections about applicability, usability, and utility of the proposed knowledge enhanced visualization processes.

Simple Summary Biomarkers for cancer diagnosis, prognosis and prediction are important tools and an urgent need in precision medicine for pancreatic cancer. In recent years, many experimental and clinical studies aimed at identifying new biomarkers for pancreatic ductal adenocarcinoma. In the review, we summarized current investigations on using novel protein markers, cell-free DNA, metabolome compounds, immune and stroma signatures and microbiome compositions as biomarkers for pancreatic cancer. Our comprehensive overview shows that although there are new promising biomarkers, CA 19-9 remains currently the only regularly used and validated biomarker for pancreatic cancer in clinical routine. Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a dismal prognosis that is frequently diagnosed at an advanced stage. Although less common than other malignant diseases, it currently ranks as the fourth most common cause of cancer-related death in the European Union with a five-year survival rate of below 9%. Surgical resection, followed by adjuvant chemotherapy, remains the only potentially curative treatment but only a minority of patients is diagnosed with locally resectable, non-metastatic disease. Patients with advanced disease are treated with chemotherapy but high rates of treatment resistance and unfavorable side-effect profiles of some of the used regimens remain major challenges. Biomarkers reflect pathophysiological or physiological processes linked to a disease and can be used as diagnostic, prognostic and predictive tools. Thus, accurate biomarkers can allow for better patient stratification and guide therapy choices. Currently, the only broadly used biomarker for PDAC, CA 19-9, has multiple limitations and the need for novel biomarkers is urgent. In this review, we highlight the current situation, recent discoveries and developments in the field of biomarkers of PDAC and their potential clinical applications.

We present an instrument to perform frequency-domain electron spin resonance (ESR) experiments that is based on coplanar waveguides and field modulation. A large parameter space in frequency (up to 25 GHz), magnetic field (up to 8 T), and temperature (down to 1.6 K) is accessible. We performed experiments on DPPH (2,2-diphenyl-1-picrylhydrazyl) as a standard to calibrate the field modulation as well as on a carbon fibre sample to estimate the overall sensitivity of the instruments. Spectra of a ruby sample in a broad frequency- and field range at cryogenic temperatures are recorded with and without field modulation. The comparison reveals the improved signal-to-noise ratio achieved by field modulation., Comment: The following article has been accepted by Review of Scientific Instruments. After it is published, it will be found at https://doi.org/10.1063/1.5141461

Medication reconciliation (MedRec) is a mission-critical process which can serve to reduce adverse drug events (ADEs) in surgical settings. However, providing quality care is limited by current health information technology (IT), which is often inefficient and unintuitive due to poor usability, resulting in high cognitive burden. We have been characterizing EHR mediated workflow in the Mayo Clinic enterprise prior to a system-wide electronic health records (EHR) conversion in order to harmonize workflows. We compared and evaluated MedRec processes in pre-operative nursing assessments across two different EHRs in place in different locales at baseline. The interfaces differed both in their modes of interaction and cognitive support. Analyses surfaced interface elements that were unintuitive and inefficient, creating unnecessary complexities in clinicians’ interactive behavior. Keystroke level models (KLM), a modeling tool for predicting task completion time, showed that to access medication lists required a different series of operations across the two systems. Different designs can differentially mediate task performance, which can aid in the mitigation of errors for complex cognitive tasks. Identification of barriers in EHR-mediated workflow and barriers to interface usability could lead to system redesigns that minimize cognitive load while improving patient safety and efficiency.

Different from more traditional policy-making avenues, the courts provide an antipolitical arena that does not require broad agreement from various constituents for policy enactment. Seeking to guide court decisions on these policy issues, individuals and organizations have filed amicus briefs that increasingly include social science to support their arguments. The Fisher v. University of Texas at Austin Supreme Court case presents an ideal example to study the use of social science evidence in amicus briefs to shape educational policy. Findings from this study identify differences in the use of social science research that suggest many ways in which our current understanding of the efforts of actors to shape educational policy via the highest court in the nation is incomplete. This study also highlights why developing this understanding could be extremely useful to both the creation of educational policy and the use of antipolitical approaches to change such policy.

Financial institutions are interested in ensuring security and quality for their customers. Banks, for instance, need to identify and stop harmful transactions in a timely manner. In order to detect fraudulent operations, data mining techniques and customer profile analysis are commonly used. However, these approaches are not supported by Visual Analytics techniques yet. Visual Analytics techniques have potential to considerably enhance the knowledge discovery process and increase the detection and prediction accuracy of financial fraud detection systems. Thus, we propose EVA, a Visual Analytics approach for supporting fraud investigation, fine-tuning fraud detection algorithms, and thus, reducing false positive alarms.

Summary Background: The 2013 American College of Cardiology / American Heart Association Guidelines for the Treatment of Blood Cholesterol emphasize treatment based on cardiovascular risk. But finding time in a primary care visit to manually calculate cardiovascular risk and prescribe treatment based on risk is challenging. We developed an informatics-based clinical decision support tool, MayoExpertAdvisor, to deliver automated cardiovascular risk scores and guideline-based treatment recommendations based on patient-specific data in the electronic heath record. Objective: To assess the impact of our clinical decision support tool on the efficiency and accuracy of clinician calculation of cardiovascular risk and its effect on the delivery of guideline-consistent treatment recommendations. Methods: Clinicians were asked to review the EHR records of selected patients. We evaluated the amount of time and the number of clicks and keystrokes needed to calculate cardiovascular risk and provide a treatment recommendation with and without our clinical decision support tool. We also compared the treatment recommendation arrived at by clinicians with and without the use of our tool to those recommended by the guidelines. Results: Clinicians saved 3 minutes and 38 seconds in completing both tasks with MayoExpertAd-visor, used 94 fewer clicks and 23 fewer key strokes, and improved accuracy from the baseline of 60.61% to 100% for both the risk score calculation and guideline-consistent treatment recommendation. Conclusion: Informatics solution can greatly improve the efficiency and accuracy of individualized treatment recommendations and have the potential to increase guideline compliance.

Introduction Rapid diagnosis accompanied by appropriate treatment is essential in the therapy of sepsis. However, there is no blood marker available, which reliably predicts sepsis and associated mortality. Therefore, the aim of the present study was to evaluate presepsin and endotoxin in comparison with established blood markers in patients undergoing emergency visceral surgery for abdominal infection. Patients and methods This prospective study included 31 patients with abdominal infection undergoing emergency surgery between March and August 2014. The Sepsis-2 and Sepsis-3 definitions of sepsis were used. Blood markers (presepsin, endotoxin, C-reactive protein, procalcitonin (PCT), interleukin 6 (IL-6), white blood count) were analyzed preoperatively and correlated with the clinical course and mortality. Additionally, a combination of the three markers, which performed best, was tested. Results Twenty patients (64.5%) in the analyzed cohort developed sepsis from an abdominal focus according to the latest sepsis definition. Out of the analyzed blood markers, presepsin exhibited the highest area under the curve, sensitivity, and specificity for the prediction of the development of sepsis. Moreover, presepsin had the highest predictive value for mortality as opposed to both endotoxin and previously established blood markers (i.e., PCT, IL-6). The multimarker approach, which included PCT, IL-6, and presepsin, showed no additional predictive value over presepsin alone. Conclusion The present study suggests that presepsin is a novel predictor of sepsis and mortality from sepsis in patients undergoing surgery for intra-abdominal infections. The findings of the present study should be validated in a larger cohort.

Plasmonic molecules are building blocks of metallic nanostructures that give rise to intriguing optical phenomena with similarities to those seen in molecular systems. The ability to design plasmonic hybrid structures and molecules with nanometric resolution would enable applications in optical metamaterials and sensing that presently cannot be demonstrated, because of a lack of suitable fabrication methods allowing the structural control of the plasmonic atoms on a large scale. Here we demonstrate a wafer-scale "lithography-free" parallel fabrication scheme to realize nanogap plasmonic meta-molecules with precise control over their size, shape, material, and orientation. We demonstrate how we can tune the corresponding coupled resonances through the entire visible spectrum. Our fabrication method, based on glancing angle physical vapor deposition with gradient shadowing, permits critical parameters to be varied across the wafer and thus is ideally suited to screen potential structures. We obtain billions of aligned dimer structures with controlled variation of the spectral properties across the wafer. We spectroscopically map the plasmonic resonances of gold dimer structures and show that they not only are in good agreement with numerically modeled spectra, but also remain functional, at least for a year, in ambient conditions.

Data quality management and assessment play a vital role for ensuring the trust in the data and its fitness-of-use for subsequent analysis. The transformation history of a data wrangling system is often insufficient for determining the usability of a dataset, lacking information how changes affected the dataset. Capturing workflow provenance along the wrangling process and combining it with descriptive information as data provenance can enable users to comprehend how these changes affected the dataset, and if they benefited data quality. We present DQProv Explorer, a system that captures and visualizes provenance from data wrangling operations. It features three visualization components: allowing the user to explore the provenance graph of operations and the data stream, the development of quality over time for a sequence of wrangling operations applied to the dataset, and the distribution of issues across the entirety of the dataset to determine error patterns.

Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open data, etc.), which makes it difficult for financial analysts to understand the big picture. In this paper, we present Sabrina, a financial data analysis and visualization approach that incorporates a pipeline for the generation of firm-to-firm financial transaction networks. The pipeline is capable of fusing the ground truth on individual firms in a region with (incremental) domain knowledge on general macroscopic aspects of the economy. Sabrina unites these heterogeneous data sources within a uniform visual interface that enables the visual analysis process. In a user study with three domain experts, we illustrate the usefulness of Sabrina, which eases their analysis process.

In patients with pancreatic ductal adenocarcinoma (PDAC), the tumor microenvironment consists of cellular and stromal components that influence prognosis. Hence, tumor-infiltrating lymphocytes (TILs) may predict prognosis more precisely than conventional staging systems. Studies on the impact of TILs are heterogeneous and further research is needed. Therefore, this study aims to point out the importance of peritumoral TILs, tumor-infiltrating neutrophils (TINs), and immune subtype classification in PDAC. Material from 57 patients was analyzed with immunohistochemistry performed for CD3, CD8, CD20, CD66b, &alpha, sma, and collagen. Hot spots with peritumoral TILs and TINs were quantified according to the QTiS algorithm and the distance of TILs hot spots to the tumor front was measured. Results were correlated with overall (OS) and progression-free survival (PFS). High densities of peritumoral hot spots with CD3+, CD8+, and CD20+ TILs correlated significantly with improved OS and PFS. Combined immune cell subtypes predicted improved OS and PFS. High infiltration of CD3+ TILs predicted progression after 12 months. The location of TILs&rsquo, hot spots and their distance to the tumor front did not correlate with patient survival. Peritumoral TILs and the composition of the stroma predict OS and PFS in PDAC.

User tasks play a pivotal role in visualization design and evaluation. However, the term ‘task’ is used ambiguously within the visualization community. In this article, we critically analyze the relevant literature and systematically compare definitions of ‘task’ and the usage of related terminology. In doing so, we identify a three-dimensional conceptual space of user tasks in visualization, referred to as the task cube, and the more precise concepts ‘objective’ and ‘action’ for tasks. We illustrate the usage of the task cube’s dimensions in an objective-driven visualization process, in different scenarios of visualization design and evaluation, and for comparing categorizations of abstract tasks. Thus, visualization researchers can better formulate their contributions which helps advance visualization as a whole.

A number of studies have investigated different ways of visualizing uncertainty. However, in the temporal dimension, it is still an open question how to best represent uncertainty, since the special characteristics of time require special visual encodings and may provoke different interpretations. Thus, we have conducted a comprehensive study comparing alternative visual encodings of intervals with uncertain start and end times: gradient plots, violin plots, accumulated probability plots, error bars, centered error bars, and ambiguation. Our results reveal significant differences in error rates and completion time for these different visualization types and different tasks. We recommend using ambiguation - using a lighter color value to represent uncertain regions - or error bars for judging durations and temporal bounds, and gradient plots - using fading color or transparency - for judging probability values.

The demand for lightweight structures in aerospace and automotive is on the rise and so are the requirements regarding the vibro-acoustic behavior. Materials with a high stiffness-to-mass ratio are often used in this field, but these materials typically have an unfavorable vibrational behavior regarding structure-borne noise. Acoustic metamaterials offer a solution for improving the vibro-acoustic performance of the structure, while the stiffness-to-mass ratio remains nearly constant. Such a metamaterial consists of periodic patterns of microstructures placed on the host structure. They modify the vibrational behavior of the host by adding local resonances and thus minimize vibrations at specific frequencies. Distinct resonances can have a major contribution to the acoustic behavior of a structure, so it is of interest to tailor microstructures, that generate stop bands at certain frequencies. This can be computationally expensive, so efficient numerical models are required for the design of acoustic metamaterials. Initial investigations show, that a parametric model order reduction (PMOR) method based on the Loewner framework can generate such models.

Background Patients with inflammatory rheumatic diseases are at higher risk for influenza and current guidelines recommend vaccination for this group of patients. The aim of this study was to evaluate the vaccination coverage and predictors for influenza vaccination among patients with inflammatory rheumatic diseases. Methods This survey was conducted at the outpatient rheumatology clinic at the Medical University of Vienna between July and October 2017. All patients diagnosed with an inflammatory rheumatic disease and receiving immunosuppressive therapy were asked to complete a questionnaire about their influenza vaccination status for 2016/17. Results 490 patients with rheumatic diseases completed a questionnaire (33% male, mean age 55.3 years). The influenza vaccination rate for the previous season was 25.3% (n = 124/490). Predictors for a positive influenza vaccination status were higher age (Adjusted Odds Ratio 5.0, 95% Confidence Interval 2.4–10.4) and treatment with biological disease-modifying antirheumatic drugs (AOR 2.0, 95% CI 1.3–3.1). Patients who received a recommendation for influenza vaccination by their general practitioner were significantly more likely to be vaccinated than those who did not (57% vs. 15%, AOR 6.6, 95% CI 4.1–10.8); even more so if they received a recommendation by their rheumatologist (62% vs. 19%, AOR 9.0, 95% CI 4.9–16.5). The main reasons for patients to decline influenza vaccination were fear of side effects (36%), concerns that vaccination might not be effective due to their immunosuppressed condition (38%) or that it might worsen the rheumatic disease (20%). Conclusions A moderate influenza vaccination rate of 25.3% was detected among patients with inflammatory rheumatic diseases. Recommendation of the influenza vaccine by a physician exerts the most effective impact on a positive vaccination status.

As the political arena becomes increasingly polarized, the legal arena is playing a more important role in the creation of education policy in the United States. One critical stage in the legal process for such efforts is at briefing where “ amici curiae ,” or friends-of-the-court, may introduce additional arguments for the court to consider through the filing of amicus curiae briefs. To explore the use of extra-legal sources by amici , we focus on the 2013 U.S. Supreme Court case Fisher v. University of Texas at Austin and ask the questions: 1) What are the types, and relative use by amici, of extra-legal sources cited in the briefs submitted in Fisher I ? and 2) What is the relative use of extra-legal sources cited in amicus briefs by supporting party and by category of amici ? Our findings reveal the wide-range of extra-legal sources used in amicus briefs, and that the type of extra-legal sources incorporated may be associated with who the amici are and which party they support. Ultimately, we discuss potential reasons for the differences observed in the use of extra-legal sources and offer recommendations to more effectively engage in the policy briefing process.

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net.

Background Disease Management Programmes (DMPs) aim to improve effectiveness and equity of care but may suffer from selective enrolment. We analysed social disparities in DMP enrolment among elderly patients with coronary heart disease (CHD) in Germany, taking into account contextual effects at municipality and primary care practice levels. Methods Cross-sectional analysis of effects of educational attainment and regional deprivation on physician-reported DMP enrolment in a subsample of a large population-based cohort study in Germany, adjusting for individual-level, practice-level and area-level variables. We calculated OR and their 95% CIs (95% CI) in cross-classified, multilevel logistic regression models. Results Among N=1280 individuals with CHD (37.3% women), DMP enrolment rates were 22.2% (women) and 35% (men). The odds of DMP enrolment were significantly higher for male patients (OR=1.98 (1.50 to 2.62)), even after adjustment for potential confounding by individual-level, practice-level and area-level variables (range: OR=1.60 (1.08 to 2.36) to 2.16 (1.57 to 2.98)). Educational attainment was not significantly associated with DMP enrolment. Compared to patients living in least-deprived municipalities, the adjusted propensity of DMP enrolment was statistically significantly lower for patients living in medium-deprived municipalities (OR=0.41 (0.24 to 0.71)), and it also tended to be lower for patients living in the most-deprived municipalities (OR=0.70 (0.40 to 1.21)). Models controlling for the social situation (instead of health-related behaviour) yielded comparable effect estimates (medium-deprived/most-deprived vs least-deprived areas: OR=0.45 (0.26 to 0.78)/OR=0.68 (0.33 to 1.19)). Controlling for differences in comorbidity attenuated the deprivation effect estimates. Conclusions We found evidence for marked gender, but not educational disparities in DMP enrolment among patients with CHD. Small-area deprivation was associated with DMP enrolment, but the effects were partly explained by differences in comorbidity. Future studies on DMPs should consider contextual effects when analysing programme effectiveness or impacts on equity and efficiency. &nbsp

We describe a novel method for automatic detection of errors in human-robot interactions. Our approach is to detect errors based on the classification of head and shoulder movements of humans who are interacting with erroneous robots. We conducted a user study in which participants interacted with a robot that we programmed to make two types of errors: social norm violations and technical failures. During the interaction, we recorded the behavior of the participants with a Kinect v1 RGB-D camera. Overall, we recorded a data corpus of 237,998 frames at 25 frames per second; 83.48% frames showed no error situation; 16.52% showed an error situation. Furthermore, we computed six different feature sets to represent the movements of the participants and temporal aspects of their movements. Using this data we trained a rule learner, a Naive Bayes classifier, and a k-nearest neighbor classifier and evaluated the classifiers with 10-fold cross validation and leave-one-out cross validation. The results of this evaluation suggest the following: (1) The detection of an error situation works well, when the robot has seen the human before; (2) Rule learner and k-nearest neighbor classifiers work well for automated error detection when the robot is interacting with a known human; (3) For unknown humans, the Naive Bayes classifier performed the best; (4) The classification of social norm violations does perform the worst; (5) There was no big performance difference between using the original data and normalized feature sets that represent the relative position of the participants.