Capturing Signs and Events Related to Catheters in Clinical Text
Doctoral thesis
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https://hdl.handle.net/11250/3104744Utgivelsesdato
2023Metadata
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Sammendrag
Annotated clinical corpora are necessary to extract information from clinical text for answering clinical questions. However, publicly available annotated clinical corpora are limited because of privacy issues, ethical concerns, and resource requirements for curating annotated corpora. When available, they are annotated for specific purposes and might lack the annotations required to answer clinical research questions. These challenges open up the opportunity to identify considerations and develop an annotated clinical corpus to answer a clinical research question.
To narrow the scope of capturing clinical concepts and knowledge within clinical text, this work focuses on a use case. The use case is capturing signs and events related to catheters from clinical adverse event notes for reducing sepsis and infection rates. This work addresses four research questions:
RQ1: What methods utilize clinical text to reduce sepsis and infections?
RQ2: What characteristics of catheter-related signs and events can be captured from clinical text?
RQ3: How can an incremental annotation-based method be developed to extract information about catheter-related signs and events from clinical text?
RQ4: How can clinical knowledge about catheter-related signs and events be captured?
Addressing the research questions resulted in four publications. The contributions are identifying research gaps, developing an annotated corpus, and developing a corresponding knowledge model. Results from this work are being extended in ongoing research.
Består av
Paper 1: Yan, Melissa; Gustad, Lise Tuset; Nytrø, Øystein. Sepsis Prediction, Early Detection and Identification Using Clinical Text for Machine Learning: A Systematic Review. JAMIA Journal of the American Medical Informatics Association 2021 ;Volum 29.(3) s. 559-575. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0) Available at: http://dx.doi.org/10.1093/jamia/ocab236Paper 2: Yan, Melissa; Høvik, Lise Husby; Pedersen, André; Gustad, Lise Tuset; Nytrø, Øystein. Preliminary Processing and Analysis of an Adverse Event Dataset for Detecting Sepsis-Related Events. I: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE 2021 ISBN 978-1-6654-0126-5. s. © 2021, IEEE. Available at: http://dx.doi.org/10.1109/BIBM52615.2021.9669410
Paper 3: Yan, Melissa Y.; Gustad, Lise Tuset; Høvik, Lise Husby; Nytrø, Øystein. Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text. I: Proceedings of the 5th Clinical Natural Language Processing Workshop (ClinicalNLP@ACL 2023). Association for Computational Linguistics 2023 ISBN 978-1-959429-88-3. s. 236-246. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) Available at: http://dx.doi.org/10.18653/v1/2023.clinicalnlp-1.29
Paper 4: Yan, Melissa Y.; Gustad, Lise Tuset; Høvik, Lise Husby; Nytrø, Øystein. Terminology and ontology development for semantic annotation: A use case on sepsis and adverse events. Semantic Web Journal 2023 ;Volum 14.(5) s. 811-871. IOS Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0). Available at: http://dx.doi.org/10.3233/SW-223226