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TimeAlign lets investigators explore the data via its simple but powerful interaction mechanisms. At the heart of it is the Align-Rank-Filter (ARF) framework. Investigators can align all patients by an important event and discover how other events are related to it. For example, by aligning patients by their first heparin exposure, 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 order patients by the number of low-platelet reading events. Finally, filtering lets investigators to narrow down patient population by event characteristics: show only patients with at least three exposures of heparin (filter by event count), or show patients who have never had a surgical procedure followed by heparin exposure followed by low-platelet count (filter by an event sequence).

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