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

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...

Full description

Saved in:
Bibliographic Details
Published in:Data (Basel) Vol. 10; no. 7; p. 104
Main Authors: Myllylä, Elisa, Siirtola, Pekka, Isosalo, Antti, Reponen, Jarmo, Tamminen, Satu, Laatikainen, Outi
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2025
Subjects:
Algorithms
Artificial intelligence
Breast cancer
Computational linguistics
Data mining
Datasets
Electronic data processing
Electronic health records
Finnish
Format
information extraction
Labeling
Language
Language processing
Machine learning
Medical records
medical text analysis
Metastasis
Methods
Natural language interfaces
Natural language processing
Parameters
Patients
radiology reports
Support vector machines
Tuning
Unstructured data
Finland
ISSN:2306-5729, 2306-5729
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Case Study-2
SourceType-Scholarly Journals-1
content type line 14
ObjectType-Feature-4
ObjectType-Report-1
ObjectType-Article-3
ISSN:2306-5729
2306-5729
DOI:10.3390/data10070104