  Multiple organ failure (MOF) is a severe syndrome with a high mortality rate
among Intensive Care Unit (ICU) patients. Early and precise detection is
critical for clinicians to make timely decisions. An essential challenge in
applying machine learning models to electronic health records (EHRs) is the
pervasiveness of missing values. Most existing imputation methods are involved
in the data preprocessing phase, failing to capture the relationship between
data and outcome for downstream predictions. In this paper, we propose
classifier-guided generative adversarial imputation networks Classifier-GAIN)
for MOF prediction to bridge this gap, by incorporating both observed data and
label information. Specifically, the classifier takes imputed values from the
generator(imputer) to predict task outcomes and provides additional supervision
signals to the generator by joint training. The classifier-guide generator
imputes missing values with label-awareness during training, improving the
classifier's performance during inference. We conduct extensive experiments
showing that our approach consistently outperforms classical and state-of-art
neural baselines across a range of missing data scenarios and evaluation
metrics.
