Do You Need Embeddings Trained on a Massive Specialized Corpus for Your Clinical Natural Language Processing Task?

Area of Science:

• Computational linguistics
• Medical informatics
• Natural Language Processing (NLP)

Background:

• Word representations are crucial for NLP tasks in the clinical domain.
• The choice of training data source can influence the effectiveness of these representations.
• French clinical NLP requires domain-specific language understanding.

Purpose of the Study:

• To investigate the impact of data source on word representations for French clinical NLP.
• To compare the performance of word embeddings and language models trained on general versus specialized clinical data.

Main Methods:

• Compared FastText word embeddings and ELMo language models.
• Trained models on general domain data (Wikipedia) and specialized data (electronic health records - EHR).
• Evaluated performance on natural language understanding and text classification tasks in French.

Main Results:

• ELMo representations trained on EHR data yielded the best performance for one of the two NLP tasks.
• Observed gains of +7% and +8% in F1-score with EHR-trained ELMo models.
• Performance varied depending on the specific NLP task and representation type.

Conclusions:

• The data source significantly impacts the effectiveness of word representations in French clinical NLP.
• Specialized electronic health record data is beneficial for training advanced language models like ELMo.
• Domain-specific training data is recommended for optimizing NLP tools in healthcare.