Silvia Miksch

Bio: Silvia Miksch is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Visual analytics & Visualization. The author has an hindex of 44, co-authored 264 publications receiving 7790 citations. Previous affiliations of Silvia Miksch include Stanford University & University of Vienna.

Visualization of Time-Oriented Data

Abstract: Time is an exceptional dimension that is common to many application domains such as medicine, engineering, business, or science Due to the distinct characteristics of time, appropriate visual and analytical methods are required to explore and analyze them This book starts with an introduction to visualization and historical examples of visual representations At its core, the book presents and discusses a systematic view of the visualization of time-oriented data along three key questions: what is being visualized (data), why something is visualized (user tasks), and how it is presented (visual representation) To support visual exploration, interaction techniques and analytical methods are required that are discussed in separate chapters A large part of this book is devoted to a structured survey of 101 different visualization techniques as a reference for scientists conducting related research as well as for practitioners seeking information on how their time-oriented data can best be visualized

Visual Methods for Analyzing Time-Oriented Data

Abstract: Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose, we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.

Modern Applied Statistics With S

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Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

Abstract: ContextDevelopers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials.ObjectivesTo review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit.Data SourcesWe updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information.Study SelectionWe included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes.Data ExtractionTeams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes.Data SynthesisOne hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001).ConclusionsMany CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.