Clinical Mobilebert I2b2 2010

🧬 ClinicalMobileBERT-i2b2-2010

ClinicalMobileBERT-i2b2-2010 is a fine - tuned model for clinical Named Entity Recognition (NER), specializing in identifying problems, treatments, and tests.

🚀 Quick Start

ClinicalMobileBERT-i2b2-2010 is a valuable tool for clinical NLP tasks. It is fine - tuned on the i2b2 - 2010 dataset based on the ClinicalMobileBERT model to recognize entities in three categories: problems, treatments, and tests.

✨ Features

• Specializes in clinical Named Entity Recognition (NER).
• Recognizes entities from three important clinical categories: problems, treatments, and tests.
• Based on the pre - trained ClinicalMobileBERT model, with around 25M parameters.

📦 Installation

No installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples were provided in the original document, so this section is skipped.

📚 Documentation

Model Description

ClinicalMobileBERT-i2b2-2010 is a fine - tuned version of the ClinicalMobileBERT model on the i2b2 - 2010 dataset for clinical Named Entity Recognition (NER). The model specialises in recognising entities from three categories: problems, treatments, and tests. The initialisation was conducted using the pre - trained checkpoints of the ClinicalMobileBERT model available on Huggingface.

Architecture

The architecture of this model is identical to ClinicalMobileBERT. The model was fine - tuned on the i2b2 - 2010 dataset for the task of clinical NER. The fine - tuning process targeted three categories of entities: problems, treatments, and tests. The model has around 25M parameters.

Use Cases

This model is useful for NLP tasks in the clinical domain that require identification and classification of problems, treatments, and tests.

🔧 Technical Details

The model is fine - tuned on the i2b2 - 2010 dataset for clinical NER. It uses the pre - trained checkpoints of the ClinicalMobileBERT model from Huggingface. The fine - tuning focuses on three entity categories: problems, treatments, and tests. With approximately 25M parameters, it is designed to efficiently handle clinical NLP tasks related to entity recognition.

📄 License

This project is licensed under the MIT license.

📖 Citation

If you use this model, please consider citing the following paper:

@article{rohanian2023lightweight,
  title={Lightweight transformers for clinical natural language processing},
  author={Rohanian, Omid and Nouriborji, Mohammadmahdi and Jauncey, Hannah and Kouchaki, Samaneh and Nooralahzadeh, Farhad and Clifton, Lei and Merson, Laura and Clifton, David A and ISARIC Clinical Characterisation Group and others},
  journal={Natural Language Engineering},
  pages={1--28},
  year={2023},
  publisher={Cambridge University Press}
}