This package provides an implementation of the prediction and analysis of early triage of critically-ill COVID-19 patients using deep learning.
More details can be found in paper: https://www.nature.com/articles/s41467-020-17280-8
Processing pipelines are implemented in python. If there is missing value, data imputation requires R.
- pandas 0.23.0
- tensorflow 1.14
- sklearn 0.21.3
- sksurv 0.11
- scipy 1.4.1
- mice_3.8.0
- dplyr_0.8.5
Data are all in csv format. Results will be saved in csv format as well.
A csv file in the following formate is needed for prediction:
| patient_ID | X.ray.abnormality | Dyspnea | Number.of.comorbidities | Malignancy | COPD | Age | NLR | Lactate.dehydrogenase | Direct.bilirubin | Creatine.kinase |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 0 | 0 | 0 | 0 | 59 | 3.428571429 | 201 | 1.7 | 26 |
| 6 | 0 | 0 | 0 | 0 | 65 | 6.545454545 | 255 | 3.3 | ||
| 9 | 0 | 4 | 0 | 0 | 61 | 3.888888889 | 211 | 3.2 | 113 |
If value is unknow, you can leave the cell empty. A imputation method will be used to fill the value. But please note that complete values will result in better prediction performance.
Explaination of each value is as following:
| column | data_type | unit | explain |
|---|---|---|---|
| patient_ID | any | ID of patients | |
| X.ray.abnormality | binary | 0/1 | 1 indicate x ray abornmality |
| Dyspnea | binary | 0/1 | 1 indicate dyspnea |
| Number.of.comorbidities | intiger | 0-9 | count of comorbidities |
| Malignancy | binary | 0/1 | 1 indicate cancer (maligant) |
| COPD | binary | 0/1 | 1 indicate COPD |
| Age | intiger | 0-150 | age of patient |
| NLR | float | ratio | Neutrophil-Lymphocyte Ratio (NLR) |
| Lactate.dehydrogenase | float | U/I | Lactate dehydrogenase test result |
| Direct.bilirubin | float | μmol/l | Direct bilirubin test result |
| Creatine.kinase | float | U/l | Creatine kinase test result |
For more details, please refer to paper.
To evaluate the result, a right censored file containing the truth of each observation is required. A csv file in the following format is needed:
| patient_ID | critically_ill | critically_ill_time |
|---|---|---|
| 1 | 0 | 5.0 |
| 2 | 0 | 10.0 |
| 3 | 0 | 6.0 |
All values are needed and the samples need to be in the same order as input csv file (patient_ID will NOT be checked in analysis). Explaination of each value is as following:
| column | data_type | unit | explain |
|---|---|---|---|
| patient_ID | any | ID of patients | |
| critically_ill | binary | 0/1 | 1 indicate critically-ill patient, right censored |
| critically_ill_time | float | days | number of days from data collection to the event |
Truth file is not needed if you only want to predict the survival ratio.
All models should be stored in models fold. Please unzip the models.zip file and make sure the files follows rule:
- code_root
- models
- fold_0
- model.meta
- fold_1
- model.meta
- fold_2
- model.meta
- fold_3
- model.meta
- fold_4
- model.meta
- imputation.csv
- fold_0
- models
Preprocessing code will impute missing value and normalize results. To run the code:
python preprocess.py input.csv
"input.csv" can be replaced with any csv file that meets input file format requirement. A file with name [INPUT]_processed.csv will be saved containing the processed file. Use this file as the input of next step.
Prediction code will predict risk scores and survival probability for each sample.
python prediction.py input.csv_processed.csv
"input.csv" can be replaced with the name of your input file. A file with name [INPUT]_processed.csv_prediction.csv will be saved containing the prediction results. Use this file as the input of next step.
5 values are generated:
| Value | Explaination |
|---|---|
| score_dl | risk score of deep learning model |
| score_final | final risk score of nomogram |
| survival_5days | 5 days critically ill probability |
| survival_10days | 10 days critically ill probability |
| survival_30days | 30 days critically ill probability |
Evaluation code will analysis predictions. To run:
python evaluation.py input.csv_processed.csv_prediction.csv truth.csv
"input.csv" can be replaced with the name of your input file. "truth.csv" can be replaced with the truth file that meet the format requirement. A file with name [INPUT]_processed.csv_prediction.csv_evaluation.csv will be saved containing the evaluation results. Use this file as the input of next step.
Following analysis will be conducted on "survival_30days" with 95% confidence inter computed as well:
| Value | Explaination |
|---|---|
| c-index | C-index of survival analysis |
| auc | ROAUC value assuming all right-censored samples are negatives |
| recall | recall value with different cut-offs assuming all right-censored samples are negatives |
This tool is for research purpose and not approved for clinical use.
This is not an official Tencent product.
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