Clinical Outcome Prediction from Admission Notes

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.

Use the CORe Model

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")

Create Admission Notes for Outcome Prediction from MIMIC-III

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

mimic_dir: Directory that contains unpacked NOTEEVENTS.csv, ADMISSIONS.csv, DIAGNOSES_ICD.csv and PROCEDURES_ICD.csv

save_dir: Any directory to save the data

admission_only: 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)

Train Outcome Prediction Tasks

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

Cite

@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/}
}

tsteffek/clinical-outcome-prediction

Clinical Outcome Prediction from Admission Notes

Use the CORe Model

Create Admission Notes for Outcome Prediction from MIMIC-III

Train Outcome Prediction Tasks

Cite