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Experimenting with Generative Adversarial Networks to Expand Sparse Physiological Time-Series Data


Baumgartner, Martin; Eggerth, Alphons; Ziegl, Andreas; Hayn, Dieter; Schreier, Günter (2020). Experimenting with Generative Adversarial Networks to Expand Sparse Physiological Time-Series Data. Studies in Health Technology and Informatics, 271:248-255.

Abstract

Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is often sparse, which renders big data analytics methods like deep learning ineffective. Data synthesis helps to augment small data sets and potentially improves patient data integrity. The presented work illustrates how Generative Adversarial Networks can be applied specifically to small data sets for enlarging sparse data. Following a state-of-the-art analysis is conducted, experimental methods with such networks are documented, which have been applied to three different data sets. Results from all three sets are presented and take-away messages are summarized. Concluding, the results' quality and limitations of the work are discussed.

Abstract

Machine Learning research and its application have gained enormous relevance in recent years. Their usage in medical settings could support patients, increase patient safety and assist health professionals in various tasks. However, medical data is often sparse, which renders big data analytics methods like deep learning ineffective. Data synthesis helps to augment small data sets and potentially improves patient data integrity. The presented work illustrates how Generative Adversarial Networks can be applied specifically to small data sets for enlarging sparse data. Following a state-of-the-art analysis is conducted, experimental methods with such networks are documented, which have been applied to three different data sets. Results from all three sets are presented and take-away messages are summarized. Concluding, the results' quality and limitations of the work are discussed.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Children's Hospital Zurich > Medical Clinic
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Physical Sciences > Biomedical Engineering
Health Sciences > Health Informatics
Health Sciences > Health Information Management
Language:English
Date:23 June 2020
Deposited On:14 Jan 2021 06:30
Last Modified:15 Jan 2021 21:02
Publisher:I O S Press
ISSN:0926-9630
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3233/SHTI200103
PubMed ID:32578570

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