| 1. |
- Danielsson, Benjamin, et al.
(author)
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Classifying Implant-Bearing Patients via their Medical Histories : a Pre-Study on Swedish EMRs with Semi-Supervised GAN-BERT
- 2022
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In: 2022 Language Resources and Evaluation Conference, LREC 2022. - : European Language Resources Association (ELRA). - 9791095546726 ; , s. 5428-5435
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Conference paper (peer-reviewed)abstract
- 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. © licensed under CC-BY-NC-4.0.
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| 2. |
- Danielsson, Bengt, et al.
(author)
-
Classifying Implant-Bearing Patients via their Medical Histories: a Pre-Study on Swedish EMRs with Semi-Supervised GAN-BERT
- 2022
-
In: LREC 2022: THIRTEEN INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION. - : EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA. - 9791095546726 ; , s. 5428-5435
-
Conference paper (peer-reviewed)abstract
- 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.
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| 3. |
- Jerdhaf, Oskar, et al.
(author)
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Evaluating Pre-Trained Language Models for Focused Terminology Extraction from Swedish Medical Records
- 2022
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In: Proceedings of the Workshop on Terminology in the 21st century. - : European Language Resources Association. - 9791095546955 ; , s. 30-32, s. 30-32
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Conference paper (peer-reviewed)abstract
- In the experiments briefly presented in this abstract, we compare the performance of a generalist Swedish pre-trained language model with a domain-specific Swedish pre-trained model on the downstream task of focused terminology extraction of implant terms, which are terms that indicate the presence of implants in the body of patients. The fine-tuning is identical for both models. For the search strategy we rely on KD-Tree that we feed with two different lists of term seeds, one with noise and one without noise. Results shows that the use of a domain-specific pre-trained language model has a positive impact on focused terminology extraction only when using term seeds without noise.
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