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Code for "Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs"

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cruiseresearchgroup/CL-ImpPreNet

 
 

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Dependencies

python == 3.8.10 pytorch == 1.11.0 numpy == 1.22.4 pandas == 1.4.3 scikit-learn == 1.1.2

Datasets

We made our experiments on the MIMIC-III and eICU datasets. In order to access the datasets, please refer to https://mimic.physionet.org/gettingstarted/access/ and https://eicu-crd.mit.edu/gettingstarted/access/.

MIMIC-III eICU
Feature Type Missingness (%) Feature Type Missingness (%)
Capillary refill rate categorical 99.78 - - -
Diastolic blood pressure continuous 30.90 Diastolic blood pressure continuous 33.80
Fraction inspired oxygen continuous 94.33 Fraction inspired oxygen continuous 98.14
Glasgow coma scale eye categorical 82.84 Glasgow coma scale eye categorical 83.42
Glasgow coma scale motor categorical 81.74 Glasgow coma scale motor categorical 83.43
Glasgow coma scale total categorical 89.16 Glasgow coma scale total categorical 81.70
Glasgow coma scale verbal categorical 81.72 Glasgow coma scale verbal categorical 83.54
Glucose continuous 83.04 Glucose continuous 83.89
Heart Rate continuous 27.43 Heart Rate continuous 27.45
Height continuous 99.77 Height continuous 99.19
Mean blood pressure continuous 31.38 Mean arterial pressure continuous 96.53
Oxygen saturation continuous 26.86 Oxygen saturation continuous 38.12
Respiratory rate continuous 26.80 Respiratory rate continuous 33.11
Systolic blood pressure continuous 30.87 Systolic blood pressure continuous 33.80
Temperature continuous 78.06 Temperature continuous 76.35
Weight continuous 97.89 Weight continuous 98.65
pH continuous 91.56 pH continuous 97.91
Age continuous 0.00 Age continuous 0.00
Admission diagnosis categorical 0.00 Admission diagnosis categorical 0.00
Ethnicity categorical 0.00 Ethnicity categorical 0.00
Gender categorical 0.00 Gender categorical 0.00

Main Entrance

main.py contains both training code and evaluation code.

Ablation Studies

We present two variants of our approach as follows:
$Ours_{\alpha}$ (A variation of our approach that does not perform graph analysis-based patient stratification modeling) :
Remove the Similarity, GCN, and InfoAgg classes
$Ours_{\beta}$ (A variation of our approach in which we omit the contrastive learning component) :
Remove the CLLoss class

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Code for "Contrastive Learning-based Imputation-Prediction Networks for In-hospital Mortality Risk Modeling using EHRs"

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