Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61 % speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.

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.

Objective: Currently, visualization support for patient data analysis is mostly limited to the representation of directly measured data. Contextual information on performed treatment steps is an important source to find reasons and explanations for certain phenomena in the measured patient data, but is mostly spared out in the analysis process. This work aims to fill this gap via integrating classical data visualization and visualization of treatment information. Methods and Material: We considered temporal as well as logical data aspects and applied a user-centered development approach that was guided by user input gathered via a user study, design reviews, and prototype evaluations. Furthermore, we investigated the novel PlanningLine glyph, that is used to represent plans in the temporal domain, via a comparative empirical user study. Results: Our interactive visualization approach CareVis provides multiple simultaneous views to cover different aspects of the complex underlying data structure of treatment plans and patient data. The tightly coupled views use visualization methods well-known to domain experts and are designed to facilitate users ’ tasks. The views are based on the concepts of clinical algorithm maps and LifeLines which have been extended in order to cope with the powerful and expressive plan representation language Asbru. Initial feedback of physicians was encouraging and is accompanied by empirical evidence which verifies that PlanningLines are well suited to manage temporal uncertainty. Conclusion: The interactive integration of different visualization methods forms a novel way of combining, relating, and analyzing different kinds of medical data and information that otherwise would be separated.

Event sequence analysis is an important task in many domains: medical researchers study the patterns of transfers within the hospital for quality control; transportation experts study accident response logs to identify best practices. In most cases they deal with more than thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called LifeFlow. LifeFlow scales to any number of records, summarizes all possible sequences, and highlights the temporal spacing of the events within sequences. We conducted two case studies with healthcare and transportation domain experts to illustrate the usefulness of LifeFlow. We also conducted a user study with ten participants which confirmed that after 15 minutes of training novice users were able to rapidly answer questions about the prevalence and temporal characteristics of sequences, find anomalies, and gain significant insight from the data.

As electronic health records (EHR) become more widespread, they enable clinicians and researchers to pose complex queries that can benefit immediate patient care and deepen understanding of medical treatment and outcomes. However, current query tools make complex temporal queries difficult to pose, and physicians have to rely on computer professionals to specify the queries for them. This paper describes our efforts to develop a novel query tool implemented in a large operational system at the Washington Hospital Center (Microsoft Amalga, formerly known as Azyxxi). We describe our design of the interface to specify temporal patterns and the visual presentation of results, then summarize the feedback gathered during early testing with physicians. The use case described in the paper focuses on adverse reactions following radiology studies using contrast.

Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called Similarity-Based Forecasting (SBF). A pattern matching search in a dataset of historical time series produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements. Keywords--- Forecasting; time series; river plot; simultaneous previews; visualization; user interfaces. 1

Graphs are a widely used and understood visualization. However, they quickly break down when the visualized data is quite complex, requiring hundreds or thousands of graphs. Our Histographs system builds on the techniques introduced with Information Murals [7] to enable meaningful visualization of such data. Histographs map the frequency of data elements at each display location to luminance, revealing data density and trends. Our improvements include contrast-weighted histogram equalization to improve the frequency-luminance mapping, splatting to make outliers visible, a second derivative modulation to reveal changes in data trends, and the use of line integral convolution show local data flow. Different data-to-space mappings can be implemented interactively. A linked correlation matrix display and highlights inter-graph relationships. Users can zoom in on data, as well as select with shape- and correlation-based brushing. Histographs are a useful way of obtaining data overviews, and revealing hidden structure in complex data sets. Keywords: information visualization, data streams, splatting, histogram equalization, image

In the advent of electronic health record (EHR) systems, physicians and clinical researchers enjoy the ease of storage, retrieval, persistence, and sharing of patient data. However, the way physicians interact with EHRs has not changed much. More specifically, task support for temporally analyzing large number of EHRs has been lacking. A number of information visualization techniques have been proposed to alleviate this problem. Unfortunately, due to their limited application to a single case study, the results are often difficult to generalize across medical scenarios. In this paper we present the usage data of and user comments on our information visualization tool Lifelines2 through eight different medical case studies. We generalize our experience into an information- seeking process model for multiple EHRs. Base on our analysis, we make recommendations to future information visualization designers for EHRs on common design requirements and future research directions.

Temporal data has always captured people’s imagination. Large databases of temporal data contain temporal patterns that can lead to the discovery of important cause-and-effect phenomena. Since discovering these patterns is a difficult task, there is a great opportunity to improve support for searching. Temporal analysis of, for example, medical records, web server logs, legal, academic, or criminal records can benefit from more effective search strategies. This dissertation describes several interactive visualization techniques designed to enhance analysts ’ experience in performing search, exploration, and summarization of multiple sets of temporal categorical data. These techniques are implemented in the software Lifelines2

The detection of known and un-known attacks usually requires the interpretation and presentation of very large amounts of timeoriented security data. Using regular means for displaying the data, such as text or tables, is often ineffective. Furthermore, displaying only raw data is not sufficient, because the security expert is still required to derive meaningful conclusions from large amounts of data. In addition, in many cases (e.g., for detecting a virus spreading in the network), an aggregated view of multiple network devices is more effective than a view of each individual device. In this paper we propose an intelligent interface used by a distributed architecture that was described in our previous work, specific to the tasks of knowledge-based interpretation, summarization, query, visualization and interactive exploration of large numbers of time-oriented data. In order to support the interpretation and computation process, we provide automated mechanisms that perform derivation of contextspecific, interval-based abstract interpretations (also known as Temporal Abstractions) from raw time-stamped security data, by using a domain-specific knowledge-base (e.g., a period of 5 hours, during the night, of a high number of FTP connections within the context of No User Activity, which might indicate the existence of a Trojan in the computer). The proposed visualization tool includes several functionalities for querying, visualization and exploration of both raw and abstracted time-oriented security data regarding single and multiple network devices.