This repository contains source code for the task creation and experiments from our paper Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration, EACL 2021.
To apply the CORe model - pre-trained on clinical outcomes - on downstream tasks, simply load it from huggingface's model hub.
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
model = AutoModel.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
Install Requirements:
pip install -r tasks/requirements.txt
Create train/val/test for e.g. Mortality Prediction:
python tasks/mp/mp.py \
--mimic_dir {MIMIC_DIR} \ # required
--save_dir {DIR_TO_SAVE_DATA} \ # required
--admission_only True \ # required
_mimicdir: Directory that contains unpacked NOTEEVENTS.csv, ADMISSIONS.csv, DIAGNOSES_ICD.csv and PROCEDURES_ICD.csv
_savedir: Any directory to save the data
_admissiononly: True=Create simulated Admission Notes, False=Keep complete Discharge Summaries
Apply these scripts accordingly for the other outcome tasks:
Length-of-Stay (los/los.py),
Diagnoses (dia/dia.py),
Diagnoses + ICD+ (dia/dia_plus.py),
Procedures (pro/pro.py) and
Procedures + ICD+ (pro/pro_plus.py)
1 - Build using Docker: Dockerfile
2 - Create Config File. See Example for Mortality Prediction: MP Example Config
3 - Run Training with Arguments
python doc_classification.py \
--task_config {PATH_TO_TASK_CONFIG.yaml} \ # required
--model_name_or_path {PATH_TO_MODEL_OR_TRANSFORMERS_MODEL_HUB_NAME} \ # required
--cache_dir {CACHE_DIR} \ # required
See doc_classification.py for optional parameters.
4 - Run Training with Hyperparameter Optimization
python hpo_doc_classification.py \
# Same parameters as above plus the following:
--hpo_samples {NO_OF_SAMPLES} \ # required
--hpo_gpus {NO_OF_GPUS} \ # required
@inproceedings{vanAken2021,
author = {Betty van Aken and
Jens-Michalis Papaioannou and
Manuel Mayrdorfer and
Klemens Budde and
Felix A. Gers and
Alexander Löser},
title = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the
Association for Computational Linguistics: Main Volume, {EACL} 2021,
Online, April 19 - 23, 2021},
pages = {881--893},
publisher = {Association for Computational Linguistics},
year = {2021},
url = {https://www.aclweb.org/anthology/2021.eacl-main.75/}
}