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dc.contributor.authorYan, Melissa Y.
dc.contributor.authorHøvik, Lise Husby
dc.contributor.authorGustad, Lise Tuset
dc.contributor.authorNytrø, Øystein
dc.date.accessioned2022-02-04T12:24:01Z
dc.date.available2022-02-04T12:24:01Z
dc.date.issued2022-01-14
dc.identifier.isbn978-1-6654-0126-5
dc.identifier.urihttps://hdl.handle.net/11250/2977187
dc.description.abstractIn the clinical domain, patient states such as sepsis due to bloodstream infection (BSI) result in observable symptoms and signs used to determine diagnosis and treatment, all of which often is documented in electronic health records. However, clinical text is brief and implicit, making it challenging to infer patient conditions by reasoning tasks and supervised machine learning. To study sepsis-related BSIs, we developed an ontology from an annotation guideline and annotated corpus that empirically captures BSIs from adverse event notes containing procedural deviations, guideline deviations, and unwanted incidents that can bring harm to patients. The resulting ontology represents (1) the physical patient state, clinical observations, and clinical documentation, and (2) background clinical knowledge for artificial intelligence, reasoning, and machine learning.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.subjectSepsisen_US
dc.subjectOntology developmenten_US
dc.subjectReasoningen_US
dc.subjectClinical knowledge representationen_US
dc.subjectAdverse eventsen_US
dc.titleUnderstanding and Reasoning About Early Signs of Sepsis - From Annotation Guideline to Ontologyen_US
dc.typeChapteren_US
dc.typePeer revieweden_US
dc.typeConference objecten_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.source.journal2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)en_US
dc.identifier.doi10.1109/BIBM52615.2021.9669311
dc.identifier.cristin1957102
dc.relation.projectNorges forskningsråd: 259055en_US


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