Comprehensive Benchmark Evaluation of Clinical Named Entity Recognition Models for French

Core Concepts

This paper presents a comprehensive benchmark evaluation of masked language models for biomedical French on the task of clinical named entity recognition, comparing their performance and environmental impact.

Abstract

The paper presents a benchmark evaluation of clinical named entity recognition (NER) models for French, using three publicly available clinical French corpora. The evaluation compares the performance of general French and multilingual masked language models (MLMs) as well as biomedical-specific French MLMs on the NER task, including the ability to handle nested entities.
Key highlights:

The CamemBERT-bio biomedical model outperforms the DrBERT biomedical model consistently, suggesting that continual pretraining from an existing French model on biomedical data can be beneficial.
The general French and multilingual models perform similarly, with lower performance compared to the biomedical models, except for the E3C corpus where FlauBERT performs better.
The frALBERT model offers a fair compromise between F-measure and carbon impact, with performance exceeding the baseline by at least 10 points and a significantly lower carbon footprint compared to other models.
On the QUAERO French Med corpus, the MLMs fail to outperform the knowledge-based approach proposed by Van Mulligen et al. (2016).
The evaluation covers both performance metrics and environmental impact, providing a comprehensive benchmark for clinical NER in French.

Stats

The DEFT corpus contains 20,360 tokens and 5,140 unique entities in the test set.
The E3C corpus contains 4,671 tokens and 706 entities in the test set.
The MEDLINE corpus contains 10,871 tokens and 3,103 entities in the test set.
The EMEA corpus contains 12,042 tokens and 2,204 entities in the test set.

Quotes

"CamemBERT-bio outperforms DrBERT consistently while FlauBERT offers competitive performance and FrAlBERT achieves the lowest carbon footprint."
"This is the first benchmark evaluation of biomedical masked language models for French clinical entity recognition that compares model performance consistently on nested entity recognition using metrics covering performance and environmental impact."

Key Insights Distilled From

A Benchmark Evaluation of Clinical Named Entity Recognition in French

by Nesr... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.19726.pdf

Deeper Inquiries

How can the insights from this benchmark evaluation be leveraged to develop more efficient and environmentally-friendly clinical NER models for other languages

The insights from this benchmark evaluation can be instrumental in developing more efficient and environmentally-friendly clinical Named Entity Recognition (NER) models for other languages by focusing on several key aspects. Firstly, the success of the CamemBERT-bio model in outperforming DrBERT and other general French models underscores the importance of domain-specific pretraining data. Therefore, for other languages, researchers can explore creating or adapting masked language models (MLMs) specifically for the biomedical domain to enhance NER performance. This approach can involve continual pretraining on relevant biomedical corpora to improve model accuracy and efficiency.
Moreover, the consideration of the carbon footprint in training and testing NER models is a crucial aspect that can be extended to other languages. By using tools like Carbon tracker to estimate and minimize the environmental impact of model training, researchers can prioritize the development of models with lower carbon emissions. This eco-conscious approach aligns with the growing emphasis on sustainability in AI research and development.
Furthermore, the comparison of MLMs with symbolic baselines in this evaluation highlights the importance of evaluating models against diverse benchmarks. For other languages, researchers can adopt a similar approach by including symbolic baselines and assessing model performance across multiple datasets to ensure robustness and generalizability. By incorporating these strategies and insights, developers can create more effective and environmentally-friendly clinical NER models for a wide range of languages.

What other factors, beyond model architecture and pretraining data, could influence the performance of clinical NER models, and how can they be incorporated into future evaluations

Beyond model architecture and pretraining data, several other factors can influence the performance of clinical NER models and should be considered in future evaluations. One crucial factor is the quality and diversity of the training data. Ensuring that the training data is representative of the target domain and covers a wide range of entity types and contexts can significantly impact model performance. Additionally, the annotation quality of the training data, including consistency and accuracy, plays a vital role in training effective NER models.
Another important factor is the fine-tuning strategy employed during model adaptation. The fine-tuning process, including hyperparameter tuning and optimization techniques, can greatly influence the model's ability to capture domain-specific nuances and improve performance on clinical NER tasks. Moreover, the choice of evaluation metrics and the incorporation of nested entity recognition assessments, as demonstrated in this benchmark evaluation, can provide a more comprehensive understanding of model capabilities and limitations.
Incorporating techniques such as data augmentation, ensemble learning, and active learning strategies can further enhance the performance of clinical NER models. Data augmentation methods can help increase the diversity of the training data, while ensemble learning can leverage the strengths of multiple models to improve overall performance. Active learning approaches can optimize the annotation process by selecting the most informative data samples for annotation, leading to more efficient model training.

Given the varying performance of the models across different corpora, how can the generalizability of clinical NER models be improved to ensure robust performance across diverse clinical datasets

To improve the generalizability of clinical NER models and ensure robust performance across diverse clinical datasets, several strategies can be implemented based on the insights gained from the varying performance of models across different corpora in the benchmark evaluation. One key approach is to enhance the diversity and representativeness of the training data by incorporating a wide range of clinical text sources and entity types. By training models on a more comprehensive and varied dataset, developers can improve the model's ability to recognize entities in different contexts and domains.
Additionally, researchers can explore transfer learning techniques to adapt models trained on one clinical dataset to perform well on others. By fine-tuning pre-trained models on specific clinical corpora, developers can leverage the knowledge learned from one dataset to improve performance on new and unseen data. This transfer learning approach can help enhance the generalizability of clinical NER models and reduce the need for extensive training on each individual dataset.
Furthermore, conducting cross-validation experiments on multiple datasets and evaluating models on a diverse set of clinical texts can provide a more comprehensive assessment of model performance and generalizability. By testing models on a wide range of data sources and domains, researchers can identify potential weaknesses and areas for improvement, leading to more robust and adaptable clinical NER models.