In digital healthcare, much patient data is available in text format. The structuring
of this data, according to standards, has yet to be widely used, including in Hungary,
where it is available in unstructured form. To make these patient records easy to
filter and search, they must be processed and structured. Using modern natural language
processing and deep learning techniques has resulted in effective systems for implementing
such workflows. However, selecting appropriate algorithms for specific text-processing
tasks is still a challenging issue. This is due to the scarcity of benchmarks and
the variety of architectures available. This article evaluates models for named entity
recognition (NER) in digital medical reports written in Hungarian. We evaluate traditional,
recurrent neural network, and transformer-based approaches for NER using a dataset
comprising 801 positron emission tomography scans and annotated medical reports. The
medical reports were annotated to cover six different entity classes and reviewed
by clinical experts to ensure accuracy. We present a comprehensive assessment of various
methods and provide insight into addressing NER problems in the case of low-resource
languages such as Hungarian.
