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Ring-Topology Echo State Networks for ICU Sepsis Classification
dc.contributor.author | Alfaras, Miquel | |
dc.contributor.author | Varandas, Rui | |
dc.contributor.author | Gamboa, Hugo | |
dc.date.accessioned | 2020-09-14T07:08:18Z | |
dc.date.available | 2020-09-14T07:08:18Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | M. Alfaras, R. Varandas and H. Gamboa, "Ring-Topology Echo State Networks for ICU Sepsis Classification," 2019 Computing in Cardiology (CinC), Singapore, Singapore, 2019, pp. Page 1-Page 4, doi: 10.23919/CinC49843.2019.9005810. | ca_CA |
dc.identifier.uri | http://hdl.handle.net/10234/189663 | |
dc.description.abstract | Sepsis is a life threatening condition that can be treated if detected early. This paper presents a study of the application of a Ring Topology Echo State Network (ESN) algorithm to a sepsis prediction task based on ICU records. The implemented algorithm is compared with commonly used classifiers and a combination of both approaches. Finally, we address how different causal strategies on filling missing record values affected the final classification performances. Having a dataset with a limited number of time entries per patient, the utility score U = 0.188 obtained (team 51: PLUX) suggests that further research is needed in order for the ESN to capture the temporal dynamics of the problem at hand. | ca_CA |
dc.format.extent | 4 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | IEEE Computer Society | ca_CA |
dc.relation.isPartOf | Computing in Cardiology (CinC), 2019 | ca_CA |
dc.rights | © Copyright IEEE | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.subject | ring topology | ca_CA |
dc.subject | echo state network | ca_CA |
dc.subject | Sepsis | ca_CA |
dc.title | Ring-Topology Echo State Networks for ICU Sepsis Classification | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.identifier.doi | http://dx.doi.org/10.23919/CinC49843.2019.9005810 | |
dc.relation.projectID | Marie Skłodowska Curie Actions ITN AffecTech: ERC H2020 Project ID: 722022; Fundação para a Ciência e a Tecnologia: FCT, Portugal; Phd grant PD/BDE/150304/2019 | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://ieeexplore.ieee.org/abstract/document/9005810 | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |