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dc.contributor.authorMuñoz Lezcano, Sergio (1)
dc.contributor.authorLópez Hernández, Fernando (1)
dc.contributor.authorCorbi, Alberto (1)
dc.date2020-12
dc.date.accessioned2021-04-14T15:28:41Z
dc.date.available2021-04-14T15:28:41Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/11186
dc.description.abstractData scientists aim to provide techniques and tools to the clinicians to manage the new coronavirus disease. Nowadays, new emerging tools based on Artificial Intelligence (AI), Image Processing (IP) and Machine Learning (ML) are contributing to the improvement of healthcare and treatments of different diseases. This paper reviews the most recent research efforts and approaches related to these new data driven techniques and tools in combination with the exploitation of the already available COVID-19 datasets. The tools can assist clinicians and nurses in efficient decision making with complex and heavily heterogeneous data, even in hectic and overburdened Intensive Care Units (ICU) scenarios. The datasets and techniques underlying these tools can help finding a more correct diagnosis. The paper also describes how these innovative AMP+ ML-based methods (e.g., conventional X-ray imaging, clinical laboratory data, respiratory monitoring and automatic adjustments, etc.) can assist in the process of easing both the care of infected patients in ICUs and Emergency Rooms and the discovery of new treatments (drugs).es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;vol. 6, nº 4
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2844es_ES
dc.rightsopenAccesses_ES
dc.subjectCOVID-19es_ES
dc.subjectdata sciencees_ES
dc.subjectMachine Learninges_ES
dc.subjectImage Processinges_ES
dc.subjectBiomarkerses_ES
dc.subjectX-Rayes_ES
dc.subjectventilationes_ES
dc.subjectJCRes_ES
dc.titleData Science Techniques for COVID-19 in Intensive Care Unitses_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.11.008


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