German Medical BERT

This is a fine-tuned model on the Medical domain for the German language and based on German BERT. This model has only been trained to improve on-target tasks (Masked Language Model). It can later be used to perform a downstream task of your needs, while I performed it for the NTS-ICD-10 text classification task.

Overview

Language model: bert-base-german-cased

Language: German

Fine-tuning: Medical articles (diseases, symptoms, therapies, etc..)

Eval data: NTS-ICD-10 dataset (Classification)

Infrastructure: Google Colab

Details

We fine-tuned using Pytorch with Huggingface library on Colab GPU.
With standard parameter settings for fine-tuning as mentioned in the original BERT paper.
Although had to train for up to 25 epochs for classification.

Performance (Micro precision, recall, and f1 score for multilabel code classification)

Models	P	R	F1
German BERT	86.04	75.82	80.60
German MedBERT-256 (fine-tuned)	87.41	77.97	82.42
German MedBERT-512 (fine-tuned)	87.75	78.26	82.73

Author

Manjil Shrestha: shresthamanjil21 [at] gmail.com

Related Paper: Report

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