Extracting Information from Unstructured Medical Reports Written in Minority Languages: A Case Study of Finnish

Elisa Myllylä, Pekka Siirtola (), Antti Isosalo, Jarmo Reponen, Satu Tamminen and Outi Laatikainen
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Elisa Myllylä: Biomimetics and Intelligent Systems Group, University of Oulu, FI-90014 Oulu, Finland
Pekka Siirtola: Biomimetics and Intelligent Systems Group, University of Oulu, FI-90014 Oulu, Finland
Antti Isosalo: Research Unit of Health Sciences and Technology, University of Oulu, FI-90014 Oulu, Finland
Jarmo Reponen: Research Unit of Health Sciences and Technology, University of Oulu, FI-90014 Oulu, Finland
Satu Tamminen: Biomimetics and Intelligent Systems Group, University of Oulu, FI-90014 Oulu, Finland
Outi Laatikainen: Medical Research Center Oulu and Oulu University Hospital, FI-90014 Oulu, Finland

Data, 2025, vol. 10, issue 7, 1-14

Abstract: In the era of digital healthcare, electronic health records generate vast amounts of data, much of which is unstructured, and therefore, not in a usable format for conventional machine learning and artificial intelligence applications. This study investigates how to extract meaningful insights from unstructured radiology reports written in Finnish, a minority language, using machine learning techniques for text analysis. With this approach, unstructured information could be transformed into a structured format. The results of this research show that relevant information can be effectively extracted from Finnish medical reports using classification algorithms with default parameter values. For the detection of breast tumour mentions from medical texts, classifiers achieved high accuracy, almost 90%. Detection of metastasis mentions, however, proved more challenging, with the best-performing models Support Vector Machine (SVM) and logistic regression achieving an F1-score of 81%. The lower performance in metastasis detection is likely due to the more complex problem, ambiguous labeling, and the smaller dataset size. The results of classical classifiers were also compared with FinBERT, a domain-adapted Finnish BERT model. However, classical classifiers outperformed FinBERT. This highlights the challenge of medical language processing when working with minority languages. Moreover, it was noted that parameter tuning based on translated English reports did not significantly improve the detection rates, likely due to linguistic differences between the datasets. This larger translated dataset used for tuning comes from a different clinical domain and employs noticeably simpler, less nuanced language than the Finnish breast cancer reports, which are written by native Finnish-speaking medical experts. This underscores the need for localised datasets and models, particularly for minority languages with unique grammatical structures.

Keywords: medical text analysis; radiology reports; machine learning; Finnish; information extraction (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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