Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA. However, the applicability of these methods to real clinical use cases is hindered, due to the limitation of pre-trained LMs in processing long textual data with thousands of words, which is a common length for a clinical note. In this work, we explore long-range adaptation from such LMs with Longformer, allowing the LMs to capture longer clinical notes context. We conduct experiments on three n2c2 challenges datasets and a longitudinal clinical dataset from Hong Kong Hospital Authority electronic health record (EHR) system to show the effectiveness and generalizability of this concept, achieving 10% F1-score improvement. Based on our experiments, we conclude that capturing a longer clinical note interval is beneficial to the model performance, but there are different cut-off intervals to achieve the optimal performance for different target variables.
Our code repository provides the implementation of four n2c2 classification tasks, i.e., n2c2 2006, n2c2 2008, n2c2 2014, and n2c2 2018. Our code enable the use of BioELECTRA, bio-lm, Longformer-adapted BioELECTRA, and Longformer-adapted bio-lm models.
To use this repo:
- Install the dependency through
pip install -r requirements.txt - Put the local dataset inside
data/n2c2_2006_smokers/*,data/n2c2_2008/*, etc - Run the finetuning. on your terminal do:
bash run_n2c2_2006_smokers.shto finetune for n2c2_2006_smokersbash run_n2c2_2008.shto finetune for n2c2_2008bash run_n2c2_2014_risk_factors.shto finetune for n2c2_2014_risk_factorsbash run_n2c2_2018_track1.shto finetune for n2c2_2018_track1
- n2c2_2006_smokers
- n2c2_2008
- n2c2_2014_risk_factors
- n2c2_2018_track1
@inproceedings{cahyawijaya-etal-2022-long,
title = "How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language Modeling",
author = "Cahyawijaya, Samuel and
Wilie, Bryan and
Lovenia, Holy and
Zhong, Huan and
Zhong, MingQian and
Ip, Yuk-Yu Nancy and
Fung, Pascale",
editor = "Lavelli, Alberto and
Holderness, Eben and
Jimeno Yepes, Antonio and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.19",
doi = "10.18653/v1/2022.louhi-1.19",
pages = "160--172",
abstract = "Large pre-trained language models (LMs) have been widely adopted in biomedical and clinical domains, introducing many powerful LMs such as bio-lm and BioELECTRA. However, the applicability of these methods to real clinical use cases is hindered, due to the limitation of pre-trained LMs in processing long textual data with thousands of words, which is a common length for a clinical note. In this work, we explore long-range adaptation from such LMs with Longformer, allowing the LMs to capture longer clinical notes context. We conduct experiments on three n2c2 challenges datasets and a longitudinal clinical dataset from Hong Kong Hospital Authority electronic health record (EHR) system to show the effectiveness and generalizability of this concept, achieving {\textasciitilde}10{\%} F1-score improvement. Based on our experiments, we conclude that capturing a longer clinical note interval is beneficial to the model performance, but there are different cut-off intervals to achieve the optimal performance for different target variables.",
}