Visual Analytics of Multivariate Intensive Care Time Series Data

Brich, Nicolas; Schulz, Christoph; Peter, Jörg; Klingert, Wilfried; Schenk, Martin; Weiskopf, Daniel; Krone, Michael

doi:10.1111/cgf.14498

Cited by 9 publications

(5 citation statements)

References 47 publications

(52 reference statements)

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“…This group included a time-series analysis (6/128, 4.7%), pattern recognition (2/128, 1.6%), and change detection (1/128, 0.8%). Time-series analysis (applied in several studies) [ 59 , 60 , 125 - 127 , 150 ] extracts statistical information from the temporal evolution of one or several variables. Pattern recognition [ 117 , 128 ] attempts to identify meaningful patterns in temporal data.…”

Section: Resultsmentioning

confidence: 99%

Patient Information Summarization in Clinical Settings: Scoping Review

Keszthelyi,

Gaudet-Blavignac,

Bjelogrlic

et al. 2023

JMIR Med Inform

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Background Information overflow, a common problem in the present clinical environment, can be mitigated by summarizing clinical data. Although there are several solutions for clinical summarization, there is a lack of a complete overview of the research relevant to this field. Objective This study aims to identify state-of-the-art solutions for clinical summarization, to analyze their capabilities, and to identify their properties. Methods A scoping review of articles published between 2005 and 2022 was conducted. With a clinical focus, PubMed and Web of Science were queried to find an initial set of reports, later extended by articles found through a chain of citations. The included reports were analyzed to answer the questions of where, what, and how medical information is summarized; whether summarization conserves temporality, uncertainty, and medical pertinence; and how the propositions are evaluated and deployed. To answer how information is summarized, methods were compared through a new framework “collect—synthesize—communicate” referring to information gathering from data, its synthesis, and communication to the end user. Results Overall, 128 articles were included, representing various medical fields. Exclusively structured data were used as input in 46.1% (59/128) of papers, text in 41.4% (53/128) of articles, and both in 10.2% (13/128) of papers. Using the proposed framework, 42.2% (54/128) of the records contributed to information collection, 27.3% (35/128) contributed to information synthesis, and 46.1% (59/128) presented solutions for summary communication. Numerous summarization approaches have been presented, including extractive (n=13) and abstractive summarization (n=19); topic modeling (n=5); summary specification (n=11); concept and relation extraction (n=30); visual design considerations (n=59); and complete pipelines (n=7) using information extraction, synthesis, and communication. Graphical displays (n=53), short texts (n=41), static reports (n=7), and problem-oriented views (n=7) were the most common types in terms of summary communication. Although temporality and uncertainty information were usually not conserved in most studies (74/128, 57.8% and 113/128, 88.3%, respectively), some studies presented solutions to treat this information. Overall, 115 (89.8%) articles showed results of an evaluation, and methods included evaluations with human participants (median 15, IQR 24 participants): measurements in experiments with human participants (n=31), real situations (n=8), and usability studies (n=28). Methods without human involvement included intrinsic evaluation (n=24), performance on a proxy (n=10), or domain-specific tasks (n=11). Overall, 11 (8.6%) reports described a system deployed in clinical settings. Conclusions The scientific literature contains many propositions for summarizing patient information but reports very few comparisons of these proposals. This work proposes to compare these algorithms through how they conserve essential aspects of clinical information and through the “collect—synthesize—communicate” framework. We found that current propositions usually address these 3 steps only partially. Moreover, they conserve and use temporality, uncertainty, and pertinent medical aspects to varying extents, and solutions are often preliminary.

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Section: Resultsmentioning

confidence: 99%

Patient Information Summarization in Clinical Settings: Scoping Review

Keszthelyi,

Gaudet-Blavignac,

Bjelogrlic

et al. 2023

JMIR Med Inform

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“…However, none of them take uncertainty into account. Visual analytics for time-oriented data has employed seasonal-trend decomposition [21] and also uncertainty quantification and visualization [22]. Visualization methods that combine aspects of time-varying data and uncertainty commonly work with time series ensembles, such as the works by Ferstl et al [23] and Van Goethem et al [24].…”

Section: Related Workmentioning

confidence: 99%

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

Krake,

Klötzl,

Hägele

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Seasonal-trend decomposition based on loess (STL) is a powerful tool to explore time series data visually. In this paper, we present an extension of STL to uncertain data, named uncertaintyaware STL (UASTL). Our method propagates multivariate Gaussian distributions mathematically exactly through the entire analysis and visualization pipeline. Thereby, stochastic quantities shared between the components of the decomposition are preserved. Moreover, we present application scenarios with uncertainty modeling based on Gaussian processes, e.g., data with uncertain areas or missing values. Besides these mathematical results and modeling aspects, we introduce visualization techniques that address the challenges of uncertainty visualization and the problem of visualizing highly correlated components of a decomposition. The global uncertainty propagation enables the time series visualization with STL-consistent samples, the exploration of correlation between and within decomposition's components, and the analysis of the impact of varying uncertainty. Finally, we show the usefulness of UASTL and the importance of uncertainty visualization with several examples. Thereby, a comparison with conventional STL is performed.

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“…MTV [22], a visual analytics system aimed at supporting human-AI collaboration in the detection, investigation, and annotation of time series anomalies 3. [23], which presents a linked-view visual analytics application for analysing high-dimensional measurement data with varying sampling rates in intensive care units. 4.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

DeepVATS: Deep Visual Analytics for Time Series

Rodríguez-Fernández¹,

Montalvo²,

Piccialli³

et al. 2023

Preprint

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The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning techniques, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data modalities and domains of application. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool has been tested on both synthetic and real datasets, and its code is publicly available on https://github.com/vrodriguezf/deepvats

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Visual Analytics of Multivariate Intensive Care Time Series Data

Cited by 9 publications

References 47 publications

Patient Information Summarization in Clinical Settings: Scoping Review

Patient Information Summarization in Clinical Settings: Scoping Review

Uncertainty-Aware Seasonal-Trend Decomposition Based on Loess

DeepVATS: Deep Visual Analytics for Time Series

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