Finding representative electrocardiogram beat morphologies with CUR

Hendryx, Emily P.; Rivière, Béatrice; Sorensen, Danny C.; Rusin, Craig G.

doi:10.1016/j.jbi.2017.12.003

Cited by 15 publications

(11 citation statements)

References 40 publications

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“…Overall, 20 sensory data simplification techniques using time-series analysis, motif discovery [ 45 ], and classification [ 46 ] methods for electrocardiogram or other types of signals…”

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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“…Overall, 20 sensory data simplification techniques using time-series analysis, motif discovery [ 45 ], and classification [ 46 ] methods for electrocardiogram or other types of signals…”

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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“…The Skeleton or Cross approximation or equivalently matrix CUR approximation (CMA) computes a low-rank approximation based on a part of individual columns and rows. It has found applications in deep learning [45], signal processing [46,47], scientific computing [48,49,50] and machine learning [51,52,53]. In the next subsequent sections, we explain how the CMA can be generalized to tensors.…”

Section: Cross Matrix Approximation (Cma) and Cross Tensor Approximat...mentioning

confidence: 99%

Cross Tensor Approximation for Image and Video Completion

Ahmadi‐Asl¹,

Asante-Mensah²,

Cichocki³

et al. 2022

Preprint

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This paper proposes a general framework to use the cross tensor approximation or tensor CUR approximation for reconstructing incomplete images and videos. The new algorithms are simple and easy to be implemented with low computational complexity. For the case of data tensors with 1) structural missing components or 2) a high missing rate, we propose an efficient smooth tensor CUR algorithms which first make the sampled fibers smooth and then apply the proposed CUR algorithms. The main contribution of this paper is to develop/investigate improved multistage CUR algorithms with filtering (smoothing ) preprocessing for tensor completion. The second contribution is a detailed comparison of the performance of image recovery for four different CUR strategies via extensive computer simulations. Our simulations clearly indicated that the proposed algorithms are much faster than most of the existing state-of-the-art algorithms developed for tensor completion, while performance is comparable and often even better.Furthermore, we will provide in GitHub developed software in MATLAB which can be used for various applications. Moreover, to our best knowledge, the CUR (cross approximation) algorithms have not been investigated nor compared till now for image and video completion.

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“…While the output of these unsupervised learners may be adequate for a given situation, there are methods being developed to help elucidate underlying statistical patterns including model-agnostic interpretability methods to describe feature importance in cluster analysis ( 52 ). For dimensionality reduction, separate algorithms have been developed that retain feature values after the dataset has been reduced into the low output space (CUR) ( 53 ). Finally, while the unsupervised learning techniques described in Part II are excellent at identifying key patient groups and features, they are not validated as a predictive model.…”

Section: Part Ii: Machine Learning To Understand Pediatric Heart Diseasementioning

confidence: 99%

Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes

et al. 2022

Self Cite

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Artificial intelligence (AI) is frequently used in non-medical fields to assist with automation and decision-making. The potential for AI in pediatric cardiology, especially in the echocardiography laboratory, is very high. There are multiple tasks AI is designed to do that could improve the quality, interpretation, and clinical application of echocardiographic data at the level of the sonographer, echocardiographer, and clinician. In this state-of-the-art review, we highlight the pertinent literature on machine learning in echocardiography and discuss its applications in the pediatric echocardiography lab with a focus on automation of the pediatric echocardiogram and the use of echo data to better understand physiology and outcomes in pediatric cardiology. We also discuss next steps in utilizing AI in pediatric echocardiography.

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Finding representative electrocardiogram beat morphologies with CUR

Cited by 15 publications

References 40 publications

Patient Information Summarization in Clinical Settings: Scoping Review

Patient Information Summarization in Clinical Settings: Scoping Review

Cross Tensor Approximation for Image and Video Completion

Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes

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