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Evaluating the Impact of Dictionary Updates on Automatic Annotations Based on Clinical NLP Systems
Yadan Fan1,2, Andrew Wen1, Feichen Shen1
1Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN, United States.
Summary
Clinical natural language processing (NLP) concept encoding performance can change with updated medical dictionaries. This study shows that manual gold standards need temporal management for accurate clinical NLP system evaluation.
Area of Science:
- Biomedical Informatics
- Clinical Natural Language Processing
Background:
- Concept encoding is vital for clinical NLP systems, enabling semantic interoperability.
- Current NLP systems often use dictionary-based approaches for concept encoding.
- Performance is typically evaluated against human-created gold standards based on existing terminologies.
Purpose of the Study:
- To evaluate the impact of updated medical dictionaries on the performance of clinical NLP systems.
- To assess annotation changes in cTAKES and MedXN due to dictionary evolution.
Main Methods:
- Compared automatic annotations from cTAKES and MedXN using updated dictionaries against existing manual gold standards.
- Analyzed annotation performance shifts resulting from dictionary updates.
Main Results:
- Dictionary updates demonstrably alter the annotation performance of clinical NLP systems.
- Significant changes in concept encoding results were observed post-dictionary updates.
Conclusions:
- Temporal management of gold standards is essential for reliable clinical NLP evaluation.
- The need for robust terminology management tools with backward compatibility is highlighted to maintain gold standard integrity.
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