Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning

This repository hosts the code of the paper Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning). In this study we introduced a unified deep learning model for ECG analysis, predicting a wide range of cardiac and non-cardiac discharge diagnoses based on the ICD10 classification system with impressive AUROC scores. Our approach excels in handling diverse diagnostic scenarios, suggesting its use as a screening tool in emergency departments, integrated into clinical decision support systems. We therefore propose the MIMIC-IV-ECG-ICD dataset derived from the MIMIC-IV and MIMIC-IV-ECG databases.

MIMIC-IV-ECG-ICD experimental workflow:

MIMIC-IV-ECG-ICD statements-distributions:

(A) represents the distribution of statements according to chapters (all percentages as relative fractions compared to the dataset size), whereas (B) represents the distribution of cardiac conditions within chapter IX.

Main ED use case investigated in manuscript statements-distributions:

(A) represents the distribution of statements according to chapters (all percentages as relative fractions compared to the dataset size), whereas (B) represents the distribution of cardiac conditions within chapter IX. However, these are the distributions of a specific ED use case (subset dataset) investigated in the manuscript.

ED subset and MIMIC-IV-ECG-ICD statistics:

Deep-learning investigated architectures:

(A) XResNet1d50 (B) S4

Datasets and experiments

1. Datasets download

Download the MIMIC-IV-ECG dataset and the MIMIC-IV dataset (with credentialed access).

2. Datasets preprocessing

Go under src/ and run the following command where your should replace the corresponding data paths

python full_preprocessing.py --mimic-path <path to mimic-iv directory ended in 'mimiciv/2.2/'> --zip-path <path to ecgs zip file> --target-path <desired output for preprocessed data default='./'>

3. Models training

We provide full commands for two experiment scenarios, you should replace the data path for your preprocessed output file of the last step. For more scenarios experimentation adapt the --finetune-dataset argument accordly see lines 32-37 in src/main_ecg.py.

These command should also export your test set predictions into a corresponding path directory (already specified in a command argument), and also save resulting AUROCs in an also specified log file.

T(ED2ALL)-E(ED2ALL)

python main_ecg.py --data <your data path> --input-size 250 --finetune-dataset mimic_ed_all_edfirst_all_2000_5A --architecture s4 --precision 32 --s4-n 8 --s4-h 512 --batch-size 32 --epochs 20 --export-predictions-path T(ED2ALL)-E(ED2ALL)/ > T(ED2ALL)-E(ED2ALL).log

T(ALL2ALL)-E(ALL2ALL)

python main_ecg.py --data <your data path> --input-size 250 --finetune-dataset mimic_all_all_allfirst_all_2000_5A --architecture s4 --precision 32 --s4-n 8 --s4-h 512 --batch-size 32 --epochs 20 --export-predictions-path T(ALL2ALL)-E(ALL2ALL)/ > T(ALL2ALL)-E(ALL2ALL).log

Results

You can find all the experimental results for each of the labels and scenarios under reports/Results_MIMIC_IV_ECG_ICD.csv

Reference

@misc{strodthoff2023cardiac,
      title={Cardiac and extracardiac discharge diagnosis prediction from emergency department ECGs using deep learning}, 
      author={Nils Strodthoff and Juan Miguel Lopez Alcaraz and Wilhelm Haverkamp},
      year={2023},
      eprint={2312.11050},
      archivePrefix={arXiv},
      primaryClass={eess.SP}
}