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Classifying Implant...
Classifying Implant-Bearing Patients via their Medical Histories: a Pre-Study on Swedish EMRs with Semi-Supervised GAN-BERT
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- Danielsson, Bengt (author)
- Linköpings universitet,Fysik, elektroteknik och matematik,Tekniska fakulteten
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- Santini, Marina (author)
- RISE Res Inst Sweden, Sweden
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- Lundberg, Peter (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Medicinsk strålningsfysik
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- Al-Abasse, Yosef (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Medicinsk strålningsfysik
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- Jönsson, Arne (author)
- Linköpings universitet,Interaktiva och kognitiva system,Tekniska fakulteten
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- Eneling, Emma (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Technology Assessment, Testing and Innovation
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- Stridsman, Magnus (author)
- Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Technology Assessment, Testing and Innovation
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(creator_code:org_t)
- EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA, 2022
- 2022
- English.
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In: LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION. - : EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. - 9791095546726 ; , s. 5428-5435
- Related links:
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https://aclanthology...
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https://urn.kb.se/re...
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Abstract
Subject headings
Close
- In this paper, we compare the performance of two BERT-based text classifiers whose task is to classify patients (more precisely, their medical histories) as having or not having implant(s) in their body. One classifier is a fully-supervised BERT classifier. The other one is a semi-supervised GAN-BERT classifier. Both models are compared against a fully-supervised SVM classifier. Since fully-supervised classification is expensive in terms of data annotation, with the experiments presented in this paper, we investigate whether we can achieve a competitive performance with a semi-supervised classifier based only on a small amount of annotated data. Results are promising and show that the semi-supervised classifier has a competitive performance when compared with the fully-supervised classifier.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Keyword
- text classification; BERT; GAN-BERT; electronic medical records; EMR; clinical text mining
Publication and Content Type
- ref (subject category)
- kon (subject category)
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