ChronoCorrelator: Enriching Events with Time Series

Dortmont, M.A.M.M. van; Elzen, Stef van den; Wijk, Jarke J. van

doi:10.1111/cgf.13697

Cited by 7 publications

(9 citation statements)

References 35 publications

(35 reference statements)

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“…More details about the items and their concrete sets of attributes are available in Multimedia Appendix 2 [15,16,18,[33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51].…”

Section: Data Itemsmentioning

confidence: 99%

“…Medical context in the corpus was mainly clinical research (13/22, 59%), clinical care only (4/22, 18%), and both areas (5/22, 22%; Table 1). Atherton et al [45], Klimov and Shahar [15], Wang et al [16], and Borhani et al [38] Clinical care 13 (59) Gschwandtner et al [18], Gotz and Wongsuphasawat et al [41], Stubbs et al [35], Tao et al [46], Gotz et al [42], Cho et al [47], Browne et al [48], Dabek et al [49], Kamaleswaran et al [40], Gomov et al [39], Wildfire et al [34], Nickerson et al [50], and Polack et al [37] Clinical research 5 (22) Guo et al [43], Rogers et al [36], van Dortmont et al [33], Magallanes et al [44], and Dahlin et al [51] Clinical research or clinical care…”

Section: Medical Contextmentioning

confidence: 99%

“…In 4% (1/22) of articles, we could not determine the temporal spread of the data. [45], Klimov and Shahar [15], Wang et al [16], Borhani et al [38], Gschwandtner et al [18], Stubbs et al [35], Gotz and Stavropoulos [42], Browne et al [48], Gomov et al [39] Wildfire et al [34], Guo et al [43], van Dortmont et al [33], and Magallanes et al [44] Laboratory 9 (41) Borhani et al [38], Stubbs et al [35], Cho et al [47], Browne et al [48], Gomov et al [39], Wildfire et al [34], Nickerson et al [50], Polack et al [37], and van Dortmont et al [33] Vital signs 8 (36) Wang et al [16], Gotz and Wongsuphasawat [41], Stubbs et al [35], Tao et al [46], Gomov et al [39], Guo et al [43], Rogers et al [36], van Dortmont et al [33], and Dahlin et al [51] Procedures 8 (36) Stubbs et al [35], Tao et al [46], Gotz and Stavropoulos [42], Dabek et al [49], Gomov et al, 2017 [39], Guo et al [43], van Dortmont et al [33], and Dahlin et al [51] Diagnosis 5 (23) Gotz and Wongsuphasawat [41], Gotz and Stavropoulos [42], Browne et al…”

Section: Datamentioning

confidence: 99%

See 2 more Smart Citations

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review

Scheer¹,

Volkert²,

Brich³

et al. 2022

J Med Internet Res

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Background Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. Objective This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. Methods This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. Results Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. Conclusions The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.

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Section: Data Itemsmentioning

confidence: 99%

Section: Medical Contextmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

See 1 more Smart Citation

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review

Scheer¹,

Volkert²,

Brich³

et al. 2022

J Med Internet Res

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show abstract

“…This results in tracing event occurrence corresponding to a significant value change in a time series. In [30], the authors present an interactive graphical tool facilitating finding correlated events with specified points in time series plots. They focus on a single event correlation and assume a consistent time scale for both types of data.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

Correlating Time Series Signals and Event Logs in Embedded Systems

Krosman

Sosnowski

2021

Sensors

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In many embedded systems, we face the problem of correlating signals characterising device operation (e.g., performance parameters, anomalies) with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based on the introduced similarity metric and deriving their distribution in time. The identified event log sequences are matched with time intervals corresponding to specified sample patterns (objects) in the registered signal time series. The matching (correlation) process involves iterative time offset adjustment. The paper presents original algorithms to investigate correlation problems using the object-oriented data models corresponding to two monitoring sources. The effectiveness of this approach has been verified in power consumption analysis using real data collected from the developed Holter device. It is quite universal and can be easily adapted to other device optimisation problems.

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“…input sequence (x t ) t anomaly score (z t ) t ground-truth anomalies (e t ) t work is closely related to recent advances in the statistical association between event series and time series [4,9,14,15,20], and uses results from event coincidence analysis (ECA) [7,12,16]. Source codes are available at https://github.com/diozaka/anomaly-eval.…”

mentioning

confidence: 99%

Statistical Evaluation of Anomaly Detectors for Sequences

Scharwächter,

Müller

2020

Preprint

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Although precision and recall are standard performance measures for anomaly detection, their statistical properties in sequential detection settings are poorly understood. In this work, we formalize a notion of precision and recall with temporal tolerance for pointbased anomaly detection in sequential data. These measures are based on time-tolerant confusion matrices that may be used to compute time-tolerant variants of many other standard measures. However, care has to be taken to preserve interpretability. We perform a statistical simulation study to demonstrate that precision and recall may overestimate the performance of a detector, when computed with temporal tolerance. To alleviate this problem, we show how to obtain null distributions for the two measures to assess the statistical significance of reported results.

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ChronoCorrelator: Enriching Events with Time Series

Cited by 7 publications

References 35 publications

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review

Correlating Time Series Signals and Event Logs in Embedded Systems

Statistical Evaluation of Anomaly Detectors for Sequences

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