The challenges of specifying intervals and absences in temporal queries

Monroe, Megan; Lan, Rongjian; Olmo, Juan Morales del; Shneiderman, Ben; Plaisant, Catherine; Millstein, Jeff

doi:10.1145/2470654.2481325

Cited by 48 publications

(34 citation statements)

References 24 publications

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“…EventFlow also includes two separate search interfaces 21 . While the basic menu-based search interface gives users easy access to either before and after relationships (Subsequence module) or during relationships (Overlap module), Advanced Search allows users to specify these relationships in tandem, as well as access to more complex temporal features such as absolute time constraints and absence of events scenarios.…”

Section: Eventflow Interface Descriptionmentioning

confidence: 99%

Visualizing Patterns of Drug Prescriptions with EventFlow: A Pilot Study of Asthma Medications in the Military Health System

Monroe¹,

Meyer²,

Plaisant³

et al. 2013

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Section: Eventflow Interface Descriptionmentioning

confidence: 99%

Visualizing Patterns of Drug Prescriptions with EventFlow: A Pilot Study of Asthma Medications in the Military Health System

Monroe¹,

Meyer²,

Plaisant³

et al. 2013

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“…EventFlow employs a graphical query interface, described in detail in [19], [20], that allows users to draw out event patterns of interest (See Figure 2). The goal of the IP formulation presented here is to match the current functionality of this query specification interface.…”

Section: Query Specificationmentioning

confidence: 99%

“…From a usability standpoint, a critical feature of search is the ability to rank results. Ranking provides structure for the result presentation, as well as helps users identify possible false positives due to errors in their query [19]. In EventFlow, the current metric for ranking results is the count of extraneous events in the record that were not matched to the query.…”

Section: Query Specificationmentioning

confidence: 99%

“…This can be accomplished relaxing the one-to-one constraint for the final query element, allowing for this element to be matched to 0 events in the patient record (Equation 18). Additionally, the category constraint must be modified to account for the scenario when this element is not matched (Equation 19). Finally, a similar consideration must be made for the ordering constraints (again, we use K to represent a large constant - Equation 20).…”

Section: Event Repetitionmentioning

confidence: 99%

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An Integer Programming Approach to Temporal Pattern Matching Queries

Monroe

Deshpande

2013

2013 IEEE 13th International Conference on Data Mining Workshops

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Abstract-Around the globe, an increasing number of our actions and activities are being recorded and stored as categorized, timestamped events. This type of data, which comprises electronic health records, shipment tracking data, and process logs, is wellsuited to the tabular structure of standard database relations. The difficulty, however, is that temporal pattern matching queries (one of the most common types of queries over this type of data) are not well-suited to standard relational query processing. This difficulty is exacerbated when the question is not only whether an event record matches a given query, but how best to match the events in the record to the events in the query when multiple options are present. In this paper, we demonstrate that temporal pattern matching queries can be formulated and solved as integer programs. This novel approach has distinct advantages over standard query processing techniques in that it puts no ordering constraints on the underlying event record, and can easily discern between multiple possible solutions using the objective function. Additionally, these integer programs can be constructed incrementally, facilitating an easy translation between the query specification interface and the underlying execution instructions. We show that integer programs can capture a wide range of temporal pattern matching constructs, including intervals, absences, repetition, and flexibility, and provide new insights on both the strengths and drawbacks of implementing this processing strategy in practice.

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“…Their request was that LifeFlow be given the capability to handle not only point events such as a heart attack or a surgery, but also interval events such as medication prescriptions and hospitals stays. This required an extensive remodeling of LifeFlow's data structure, display rendering, and the mechanisms for exploring and querying the data [17]. Happy to oblige, however, the application was restructured and redubbed as EventFlow [18](see Figure 2).…”

Section: Introductionmentioning

confidence: 99%

Temporal Event Sequence Simplification

Monroe

Lan

Lee

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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241

178

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Fig. 1: In EventFlow, the original LABA dataset, consisting of over 2700 visual elements (left), was quickly pared down to the events most critical to the study. The simplified dataset (right) consists of only 492 visual elements, an 80% reduction in visual complexity. From this simplified figure, aligned by the patients' "new" LABA prescription, researchers were immediately able to notice the data sparsity on the left side of the alignment point, indicating that patients had not received other treatments in the months leading up to their LABA prescription (i.e. not following the recommended practices).Abstract-Electronic Health Records (EHRs) have emerged as a cost-effective data source for conducting medical research. The difficulty in using EHRs for research purposes, however, is that both patient selection and record analysis must be conducted across very large, and typically very noisy datasets. Our previous work introduced EventFlow, a visualization tool that transforms an entire dataset of temporal event records into an aggregated display, allowing researchers to analyze population-level patterns and trends. As datasets become larger and more varied, however, it becomes increasingly difficult to provide a succinct, summarizing display. This paper presents a series of user-driven data simplifications that allow researchers to pare event records down to their core elements. Furthermore, we present a novel metric for measuring visual complexity, and a language for codifying disjoint strategies into an overarching simplification framework. These simplifications were used by real-world researchers to gain new and valuable insights from initially overwhelming datasets.

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The challenges of specifying intervals and absences in temporal queries

Cited by 48 publications

References 24 publications

Visualizing Patterns of Drug Prescriptions with EventFlow: A Pilot Study of Asthma Medications in the Military Health System

Visualizing Patterns of Drug Prescriptions with EventFlow: A Pilot Study of Asthma Medications in the Military Health System

An Integer Programming Approach to Temporal Pattern Matching Queries

Temporal Event Sequence Simplification

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