About

Interfaces

This algorithm implements all of the following input-output combinations:

Challenge Performance

Model Facts

Summary

This algorithm is an adaptation of the DRAGON baseline (version 0.2.1), with pretrained foundational model joeranbosma/dragon-bert-base-domain-specific. This algorithm is used for the pretraining experiment in the DRAGON manuscript [1], according to the pre-specified statistical analysis plan [2]. See dragon.grand-challenge.org/manuscript for the latest info on the DRAGON manuscript. Please cite the manuscript [1] when using this model.

[1] J. S. Bosma, K. Dercksen, L. Builtjes, R. André, C, Roest, S. J. Fransen, C. R. Noordman, M. Navarro-Padilla, J. Lefkes, N. Alves, M. J. J. de Grauw, L. van Eekelen, J. M. A. Spronck, M. Schuurmans, A. Saha, J. J. Twilt, W. Aswolinskiy, W. Hendrix, B. de Wilde, D. Geijs, J. Veltman, D. Yakar, M. de Rooij, F. Ciompi, A. Hering, J. Geerdink, H. Huisman, DRAGON Consortium. The DRAGON Benchmark for Clinical NLP. Under review.

[2] J. S. Bosma, K. Dercksen, L. Builtjes, R. André, C, Roest, S. J. Fransen, C. R. Noordman, M. Navarro-Padilla, J. Lefkes, N. Alves, M. J. J. de Grauw, L. van Eekelen, J. M. A. Spronck, M. Schuurmans, A. Saha, J. J. Twilt, W. Aswolinskiy, W. Hendrix, B. de Wilde, D. Geijs, J. Veltman, D. Yakar, M. de Rooij, F. Ciompi, A. Hering, J. Geerdink, H. Huisman, DRAGON Consortium (2024). DRAGON Statistical Analysis Plan (v1.0). Zenodo. https://doi.org/10.5281/zenodo.10374512

Mechanism

For details on the pretrained foundational model, check out HuggingFace: joeranbosma/dragon-bert-base-domain-specific.

The following settings were used in the the DRAGON baseline:

model_name = "joeranbosma/dragon-bert-base-domain-specific"
per_device_train_batch_size = 4
gradient_accumulation_steps = 2
gradient_checkpointing = False
max_seq_length = 512
learning_rate = 1e-05

Validation and Performance

This model was tested on the 28 tasks in the DRAGON Benchmark for clinical NLP. For the full test leaderboard see here and for the performance of this model see here.

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

You should anonymize your reports before uploading them to Grand Challenge.

Common Error Messages

Some logs are incorrectly seen as warnings, so each successful algorithm job will still say "Succeeded, with warnings". This warning can typically be ignored.

Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3