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dc.contributor.authorMarchuk, Yaroslav
dc.contributor.authorMagrans, Rudys
dc.contributor.authorSales, Bernat
dc.contributor.authorMontanyà, Jaume
dc.contributor.authorLópez-Aguilar, Josefina
dc.contributor.authorDe Haro, Candelaria
dc.contributor.authorGomà Fernández, Gemma
dc.contributor.authorSubirà Cuyàs, Carles
dc.contributor.authorFernández Fernández, Rafael
dc.contributor.authorKacmarek, Robert M.
dc.contributor.authorBlanch, Lluís
dc.date.accessioned2019-12-23T15:39:11Z
dc.date.available2019-12-23T15:39:11Z
dc.date.issued2018-12-04
dc.identifier.citationMarchuk, Yaroslav; Magrans, Rudys; Sales, Bernat [et al.]. Predicting patient-ventilator asynchronies with hidden markov models. Scientific Reports, 2018, vol. 8, p. 1-7. Disponible en: <https://www.nature.com/articles/s41598-018-36011-0#rightslink>. Fecha de acceso: 23 dic. 2019. DOI: 10.1038/s41598-018-36011-0ca
dc.identifier.issn2045-2322ca
dc.identifier.urihttp://hdl.handle.net/20.500.12328/1406
dc.description.abstractIn mechanical ventilation, it is paramount to ensure the patient’s ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) – z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.ca
dc.format.extent7ca
dc.language.isoengca
dc.publisherSpringer Natureca
dc.relation.ispartofScientific Reportsca
dc.relation.ispartofseries8;
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.ca
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.otherEnginyeria biomèdicaca
dc.subject.otherMineria de dades
dc.subject.otherMedicina preventiva
dc.subject.otherEstadística
dc.subject.otherIngeniería biomédica
dc.subject.otherProcesamiento de datos
dc.subject.otherMedicina preventiva
dc.subject.otherEstadística
dc.subject.otherBiomedical engineering
dc.subject.otherData mining
dc.subject.otherPreventive Medicine
dc.subject.otherStatistical methods
dc.subject.otherStatistics
dc.titlePredicting patient-ventilator asynchronies with hidden markov modelsca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/acceptedVersionca
dc.embargo.termscapca
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/2PE/RTC-2017-6193-1ca
dc.subject.udc61ca
dc.identifier.doihttps://dx.doi.org/10.1038/s41598-018-36011-0ca


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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