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TimeAlign lets investigators explore the data via its simple but powerful interaction mechanisms. At the heart is the Align-Rank-Filter (ARF) framework. Investigators can align all patients by some important first occurrence of an event and discover how other events are related to it. For example, by aligning patients by their first heparin injection, investigators can examine if the event of low-platelet reading occur more frequently as a potential signal for heparin-induced thrombocytopenia. Ranking, for example, can reorder patients by the number of low-platelet reading events from highest to lowest. Finally, filtering lets investigators to narrow down patient population by event characteristics: show me only patients with at least three exposures of heparin (filter by event count), or show me patients who had have never had a surgical procedure followed by heparin exposure followed by low-platelet count (filter by event ordering).

In addition, distribution of events over time can be shown. Groups of patients can be created via filtering. Distribution of groups can be compared. All these features let investigators visualize and interact with the data, explore potentially temporal relationships, formulate hypotheses, and hopefully discover new findings.

History

i2b2's TimeAlign plugin originates from the Lifelines2 project from the Human-computer Interaction Lab in the University of Maryland at College Park. Lifelines2 was a PhD dissertation project from Taowei David Wang, under the supervision of Ben Shneiderman and Catherine Plaisant. Taowei has since graduated and is now part of the broader i2b2 team, creating different plugins for the i2b2 platform.

Download and Setup

Quick Start

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