The challenges of living with and managing epidermolysis bullosa: insights from patients and caregivers.

BACKGROUND: Little information is available regarding the burden of living with and managing epidermolysis bullosa, including the distinct challenges faced by patients with different disease types/subtypes. METHODS: A 90-question/item survey was developed to collect demographics, diagnostic data, management practices, and burden of illness information for patients with epidermolysis bullosa living in the United States. Recruitment was conducted via email and social media in partnership with epidermolysis bullosa patient advocacy organizations in the United States, and the survey was conducted via telephone interview by a third-party health research firm. Respondents aged ≥ 18 years with a confirmed diagnosis of epidermolysis bullosa or caring for a patient with a confirmed diagnosis of epidermolysis bullosa were eligible to participate in the survey. RESULTS: In total, 156 responses were received from patients (n = 63) and caregivers (n = 93) representing the epidermolysis bullosa types of simplex, junctional, and dystrophic (subtypes: dominant and recessive). A large proportion of patients (21%) and caregivers (32%) reported that the condition was severe or very severe, and 19% of patients and 26% of caregivers reported a visit to an emergency department in the 12 months prior to the survey. Among the types/subtypes represented, recessive dystrophic epidermolysis bullosa results in the greatest wound burden, with approximately 60% of patients and caregivers reporting wounds covering > 30% of total body area. Wound care is time consuming and commonly requires significant caregiver assistance. Therapeutic options are urgently needed and reducing the number and severity of wounds was generally ranked as the most important treatment factor. CONCLUSIONS: Survey responses demonstrate that epidermolysis bullosa places a considerable burden on patients, their caregivers, and their families. The limitations caused by epidermolysis bullosa mean that both patients and caregivers must make difficult choices and compromises regarding education, career, and home life. Finally, survey results indicate that epidermolysis bullosa negatively impacts quality of life and causes financial burden to patients and their families.

Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration.

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop.

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.