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Focused Terminology Extraction for CPSs: The Case of "Implant Terms" in Electronic Medical Records
Linköpings universitet, Institutionen för datavetenskap. Linköpings universitet, Tekniska fakulteten.
Digital Health, RISE, Sweden.
Linköpings universitet, Institutionen för hälsa, medicin och vård, Avdelningen för diagnostik och specialistmedicin. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Diagnostikcentrum, Medicinsk strålningsfysik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0001-8661-2232
Linköpings universitet, Institutionen för datavetenskap, Interaktiva och kognitiva system. Linköpings universitet, Tekniska fakulteten. Region Östergötland, Diagnostikcentrum, Medicinsk strålningsfysik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
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2021 (engelsk)Inngår i: 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), 2021Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2021.
Serie
IEEE International Conference on Communications Workshops, ICC, ISSN 2164-7038, E-ISSN 2694-2941
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Identifikatorer
URN: urn:nbn:se:liu:diva-184609DOI: 10.1109/ICCWorkshops50388.2021.9473700ISI: 000848412200183Scopus ID: 2-s2.0-85112819503ISBN: 9781728194417 (digital)ISBN: 9781728194424 (tryckt)OAI: oai:DiVA.org:liu-184609DiVA, id: diva2:1654435
Konferanse
2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, 14-23 June, 2021
Merknad

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

Tilgjengelig fra: 2022-04-27 Laget: 2022-04-27 Sist oppdatert: 2022-10-26bibliografisk kontrollert

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Lundberg, PeterKarlsson, AnetteJönsson, Arne

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