Custom Visualization without Real Programming

Information Visualization tools have simplified visualization development. Some tools help simple users construct standard visualizations; others help programmers develop custom visualizations. This thesis contributes to the field of Information Visualization and End-User Development. The first contribution of the thesis is a taxonomy for Information Visualization development tools. Existing taxonomies for Information Visualization are helpful, but none of them can properly categorize visualization tools from a user development perspective. The categorization of 20 Information Visualization tools proves the applicability of this taxonomy, and the result showed that there are no Drag-and-Drop tools that allow end-user developers as well as programmers to create custom visualizations. The results can be used by the End-User Development and the Information Visualization community to identify future avenues of research. The second contribution is a new visualization development approach, the Drag-Drop-Set-View-Interact approach provided by the visualization too

De-identifying an EHR Database - Anonymity, Correctness and Readability of the Medical Record

Abstract. Electronic health records (EHR) contain a large amount of structured data and free text. Exploring and sharing clinical data can improve healthcare and facilitate the development of medical software. However, revealing confidential information is against ethical principles and laws. We de-identified a Danish EHR database with 437,164 patients. The goal was to generate a version with real medical records, but related to artificial persons. We developed a de-identification algorithm that uses lists of named entities, simple language analysis, and special rules. Our algorithm consists of 3 steps: collect lists of identifiers from the database and external resources, define a replacement for each identifier, and replace identifiers in structured data and free text. Some patient records could not be safely de-identified, so the de-identified database has 323,122 patient records with an acceptable degree of anonymity, readability and correctness (F-measure of 95%). The algorithm has to be adjusted for each culture, language and database

Preserving medical correctness, readability and consistency in de-identified health records

A health record database contains structured data fields that identify the patient, such as patient ID, patient name, e-mail and phone number. These data are fairly easy to de-identify, that is, replace with other identifiers. However, these data also occur in fields with doctors’ free-text notes written in an abbreviated style that cannot be analyzed grammatically. If we replace a word that looks like a name, but isn’t, we degrade readability and medical correctness. If we fail to replace it when we should, we degrade confidentiality. We de-identified an existing Danish electronic health record database, ending up with 323,122 patient health records. We had to invent many methods for de-identifying potential identifiers in the free-text notes. The de-identified health records should be used with caution for statistical purposes because we removed health records that were so special that they couldn’t be de-identified. Furthermore, we distorted geography by replacing zip codes with random zip codes.</jats:p

Considering agency and data granularity in the design of visualization tools

The Ecuadorian Government supports Gonzalo Gabriel Méndez through a SENESCYT scholarship.Previous research has identified trade-offs when it comes to designing visualization tools. While constructive “bottom-up” tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space.Postprin