Negation's not solved: generalizability versus optimizability in clinical natural language processing.

Area of Science:

• Clinical Natural Language Processing (NLP)
• Machine Learning in Healthcare
• Computational Linguistics

Background:

• The task of negation detection in clinical NLP is often considered solved.
• However, optimizable solutions do not guarantee generalizable performance across different clinical text domains.

Purpose of the Study:

• To investigate the generalizability of negation detection models in clinical text.
• To introduce and evaluate a machine learning-based Polarity Module for negation detection.
• To identify factors contributing to the lack of generalizability.

Main Methods:

• Development of a machine learning-based Polarity Module.
• Extensive performance comparison of the module across four manually annotated clinical text corpora.
• Analysis of factors influencing generalizability, including annotation guidelines, entity characteristics, data volume, and linguistic context.

Main Results:

• Negation detection performance significantly degrades without in-domain development or training data.
• No single factor fully explains the difficulty in achieving generalizability.
• Challenges persist in determining optimal data utilization strategies (single source, combined models, or domain adaptation).

Conclusions:

• Optimizing negation detection performance requires in-domain manual annotation or rule modification.
• Generalizability remains a key challenge in clinical NLP negation detection.
• Future research should focus on domain-adaptive and task-adaptive methods for clinical NLP.