Practical Solutions to Improve Healthcare NER Accuracy
Entity recognition in the medical domain often leads to accuracy issues due to terminology complexity and text diversity. The following solutions are available through the OpenMed platform:
- Selecting a Domain Adaptation Model: Priority is given to models that have areas of specialization indicated in their names, such asOpenMed-NER-PharmaDetect-SuperClinical-434MOptimized for drug recognition, F1 scores higher than generic models on test set 36%
- Parameters scale to match needs: The 434M parametric version is recommended for clinical-level applications, and the 65M lightweight version can be used for research-level analyses by testing different models of theentity['score']Confidence level for value assessment
- Post-processing optimization: Utilizing pipeline'saggregation_strategyParameters (optional 'simple'/'first', etc.) to merge fragmented recognition results, especially for Chinese compound word recognition.
- Area fine-tuningFor special scenarios such as rare diseases, the OpenMed model can be used as a base and fine-tuned by Hugging Face Trainer with your own labeled data (5-10 hours of GPU time).
Typical optimization case: When dealing with "EGFR gene exon 19 deletion mutation", the OncologyDetect series model is used to improve the accuracy by 28% compared with the general model, and the batch_size=4 parameter is used to balance the GPU memory consumption.
This answer comes from the articleOpenMed: an open source platform for free AI models in healthcareThe
May not be reproduced without permission:AI productivity tools " How to address the lack of accuracy in recognizing key entities in medical text?
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