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Focused Terminology Extraction for CPSs: The Case of "Implant Terms" in Electronic Medical Records
Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
Digital Health, RISE, Sweden.
Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0001-8661-2232
Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
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2021 (English)In: Proceedings of the IEEE International Conference on Communications Workshop on Communication, Computing, and Networking in Cyber-Physical Systems (IEEE CCN-CPS 2021), Institute of Electrical and Electronics Engineers (IEEE), 2021Conference paper, Published paper (Refereed)
Abstract [en]

Language Technology is an essential component of many Cyber-Physical Systems (CPSs) because specialized linguistic knowledge is indispensable to prevent fatal errors. We present the case of automatic identification of implant terms. The need of an automatic identification of implant terms spurs from safety reasons because patients who have an implant may or may be not submitted to Magnetic Resonance Imaging (MRI). Normally, MRI scans are safe. However, in some cases an MRI scan may not be recommended. It is important to know if a patient has an implant, because MRI scanning is incompatible with some implants. At present, the process of ascertain whether a patient could be at risk is lengthy, manual, and based on the specialized knowledge of medical staff. We argue that this process can be sped up, streamlined and become safer by sieving through patients' medical records. In this paper, we explore how to discover implant terms in electronic medical records (EMRs) written in Swedish with an unsupervised approach. To this aim we use BERT, a state-of-the-art deep learning algorithm based on pre-trained word embeddings. We observe that BERT discovers a solid proportion of terms that are indicative of implants.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2021.
Series
IEEE International Conference on Communications Workshops, ICC, ISSN 2164-7038, E-ISSN 2694-2941
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:liu:diva-184609DOI: 10.1109/ICCWorkshops50388.2021.9473700ISI: 000848412200183Scopus ID: 2-s2.0-85112819503ISBN: 9781728194417 (electronic)ISBN: 9781728194424 (print)OAI: oai:DiVA.org:liu-184609DiVA, id: diva2:1654435
Conference
2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, 14-23 June, 2021
Note

Funding: Vinnova (Swedens innovation agency) [2020-00228]; Swedish Ethical Review Authority [2021-00890]

Available from: 2022-04-27 Created: 2022-04-27 Last updated: 2022-10-26Bibliographically approved

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Lundberg, PeterKarlsson, AnetteJönsson, Arne

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Jerdhaf, OskarLundberg, PeterKarlsson, AnetteJönsson, Arne
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Department of Computer and Information ScienceFaculty of Science & EngineeringDivision of Diagnostics and Specialist MedicineFaculty of Medicine and Health SciencesMedical radiation physicsCenter for Medical Image Science and Visualization (CMIV)Human-Centered systems
Language Technology (Computational Linguistics)

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CiteExportLink to record
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Citation style
  • apa
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Output format
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