Classifying Implant-Bearing Patients via their Medical Histories : a Pre-Study on Swedish EMRs with Semi-Supervised GAN-BERT

• 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

Nyckelord

BERT

clinical text mining

electronic medical records

EMR

GAN-BERT

text classification

Classification (of information)

Data mining

Medical computing

Text processing

Electronic medical record

Medical record

Semi-supervised

Supervised classifiers

Text-mining

Support vector machines

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)