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A Less Traditional Approach to Biomedical Signal Processing for Sepsis Prediction

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dc.contributor.author IAPĂSCURTĂ, V.
dc.date.accessioned 2021-11-12T10:59:44Z
dc.date.available 2021-11-12T10:59:44Z
dc.date.issued 2021
dc.identifier.citation IAPĂSCURTĂ, V. A Less Traditional Approach to Biomedical Signal Processing for Sepsis Prediction. In: ICNMBE-2021: the 5th International Conference on Nanotechnologies and Biomedical Engineering, November 3-5, 2021: Program and abstract book. Chişinău, 2021, p. 78. ISBN 978-9975-72-592-7. en_US
dc.identifier.isbn 978-9975-72-592-7
dc.identifier.uri http://repository.utm.md/handle/5014/17998
dc.description Only Abstract. en_US
dc.description.abstract Most of the data generated by monitors in a clinical setting represent time series data which can be visualized and subsequently used for decision making. This usually is the simplest part. A more challenging aspect is using this data for more complex task like machine learning with the same goal – computer assisted decisions. Within this challenge raw biomedical signal data need to be preprocessed before being passed to the machine learning algorithm. This can be done by a multitude of methods. A number of such methods comes from the field of Algorithmic Complexity and although of a promising nature, these particular methods are poorly explored yet. The current research presents an example of applying the Block Decomposition Method to data routinely generated by patients in a modern Intensive Care Unit. The final goal of a larger research, the actual research being part of, is building a system for early sepsis prediction. en_US
dc.language.iso en en_US
dc.publisher Universitatea Tehnică a Moldovei en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject Block Decomposition Method en_US
dc.subject Intensive Care Unit en_US
dc.subject sepsis prediction en_US
dc.title A Less Traditional Approach to Biomedical Signal Processing for Sepsis Prediction en_US
dc.type Article en_US


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