Generating Healthcare Reports Using Natural Language Processing : Fine-tuning Finnish Language Models
Year: 2025
Type: dissertation
Abstract: Generating reports in different healthcare departments is an important factor in keeping up with different phenomena occurring in the everyday work. Examples of these are the number of operations done, complications happened during them, and used instruments. Different systems the healthcare workers use might have their own reporting systems, which is the case for Tampere heart hospital’s cardiology and cardiothoracic surgery departments with their quality monitoring and reporting system called Kardio. The current problem with Kardio has been limited possibilities to generate reports and therefore a solution was to try and develop a system where users could generate reports by natural language prompts. The goal was to train Finnish language models so that they could be used to identify named entities for cardiology operations. The results obtained have been used to investigate whether such a feature could be used as a part in generating new reports. The implementation of the functionality began by considering the components required for its development. This led to the conclusion that a tool capable of identifying certain entities from natural language text was necessary. There are no language models based on medical text and data for the Finnish language readily available and training a whole new language model was not necessary. Because of this, the choice was to fine-tune existing Finnish language models with data related to the field of cardiology. The chosen three language models to be fine-tuned were two BERT based, FinBERT and Finnish RoBERTa, and one CNN-based language model by spaCy. The results showed that there are no significant differences in the fine-tuning of the language models, but the F1-scores indicate that FinBERT and Finnish RoBERTa perform slightly better than spaCy. The F1-scores, ranging around 0.95 to 0.98, were very good compared to the fine-tuning of English language models, but this was possibly due to the very limited and self-generated training data. Based on the results obtained, the fine-tuned language model could be used in recognising entities needed for cardiology operation report generation after further development. Additional work should be done to improve the training data so that the language model recognises all the entities needed for reports, while ensuring that the language model performs well with a wide range of inputs.
Author: Laitinen, Tuuli
Institution: Tampere University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7133049232