Sharing models and tools for processing German clinical texts.

Area of Science:

• Natural Language Processing (NLP)
• Computational Linguistics
• Medical Informatics

Background:

• Automatic processing of non-English clinical documents is hindered by a scarcity of public medical language resources.
• Existing NLP tools often lack sufficient training data for specialized domains like German clinical texts.

Purpose of the Study:

• To address the lack of resources for German clinical NLP by proposing the sharing of statistical models.
• To develop and evaluate NLP components for sentence splitting, tokenization, and Part-of-Speech (POS) tagging of German clinical documents.

Main Methods:

• Training of sentence splitting, tokenization, and POS tagging models using the confidential FRAMED corpus.
• Utilizing statistical models derived from access-protected German clinical documents.
• Comparative evaluation against established NLP toolkits (OpenNLP, Stanford POS tagger).

Main Results:

• The developed models trained on the FRAMED corpus demonstrate superior performance.
• Outperformed alternative components from OpenNLP and the Stanford POS tagger on the same dataset.
• Successfully provided functional NLP components for German clinical text processing.

Conclusions:

• Sharing statistical models derived from protected data is a viable strategy to overcome resource limitations in specialized NLP tasks.
• The proposed models offer improved performance for fundamental NLP tasks in the German clinical domain.
• Facilitates further research and development in clinical NLP for under-resourced languages.