Mimicking a Psychiatric Disorder: Heroin-Induced Leukoencephalopathy.

Toxic leukoencephalopathy is a rare cause of encephalopathy. We describe two cases of toxic leukoencephalopathy associated with opiate abuse where they were misdiagnosed and admitted to the psychiatric ward. In our case series, both patients presented with behavioral changes, restlessness, pressured speech, and cognitive impairment for which they were initially labeled as psychiatric patients and were treated as such. However, after an extensive workup to elucidate the etiology of alteration in mental status, toxic leukoencephalopathy associated with heroin abuse was found to be the culprit in both cases. Toxic leukoencephalopathy is a rare condition that can be caused by inhalation of heroin. Clinically, it may present with confusion, behavioral changes, extrapyramidal symptoms, generalized motor deficit, unresponsiveness and even death. Our cases highlight the importance of recognizing the psychiatric presentation of toxic leukoencephalopathy.

An insight-based longitudinal study of visual analytics.

Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term usage of the tools. A longitudinal study of a bioinformatics data set analysis is reported here. The main focus of this work is to capture the entire analysis process that an analyst goes through from a raw data set to the insights sought from the data. The study provides interesting observations about the use of visual representations and interaction mechanisms provided by the tools, and also about the process of insight generation in general. This deepens our understanding of visual analytics, guides visualization developers in creating more effective visualization tools in terms of user requirements, and guides evaluators in designing future studies that are more representative of insights sought by users from their data sets.

An insight-based methodology for evaluating bioinformatics visualizations.

High-throughput experiments, such as gene expression microarrays in the life sciences, result in very large data sets. In response, a wide variety of visualization tools have been created to facilitate data analysis. A primary purpose of these tools is to provide biologically relevant insight into the data. Typically, visualizations are evaluated in controlled studies that measure user performance on predetermined tasks or using heuristics and expert reviews. To evaluate and rank bioinformatics visualizations based on real-world data analysis scenarios, we developed a more relevant evaluation method that focuses on data insight. This paper presents several characteristics of insight that enabled us to recognize and quantify it in open-ended user tests. Using these characteristics, we evaluated five microarray visualization tools on the amount and types of insight they provide and the time it takes to acquire it. The results of the study guide biologists in selecting a visualization tool based on the type of their microarray data, visualization designers on the key role of user interaction techniques, and evaluators on a new approach for evaluating the effectiveness of visualizations for providing insight. Though we used the method to analyze bioinformatics visualizations, it can be applied to other domains.