official implementation of "Eliminating Indefiniteness of Clinical Spectrum for Better Screening COVID-19", accepted by IEEE Journal of Biomedical and Health Informatics (JBHI2021).
To install the environment via Anaconda:
conda env create -f environment.yaml
the original dataset is in https://www.kaggle.com/einsteindata4u/covid19
cd ./data
unzip feature_original.zip
To train the models in this paper, run this command:
cd ./tools
python main_multiple.py
The best performed model is in
./checkpoint_best.pth
The comparison experiments are
cd ./comparison
python comparison_experiments_fill_0_multimetric.py
For 10-fold cross-validation, our model achieves high performance on the COVID-19 Clinical dataset. The table below shows the results in the paper.
| Accuracy | Recall | Precision | AUC | |
|---|---|---|---|---|
| Results in paper | 94.80±1.98 | 92.79±3.07 | 92.97±3.06 | 94.93±2.00 |
Recently, we introduce F1-score as the metric for selecting the best model, the performance in terms of Recall and Precision has improved. As mentioned in the paper, Recall is the most important metric in this paper.
| Accuracy | Recall | Precision | AUC | F1 | |
|---|---|---|---|---|---|
| Results F1 | 94.05±2.17 | 95.99±3.69 | 94.42±2.26 | 90.50±3.76 | 93.81±2.52 |