  Recent work in machine learning for healthcare has raised concerns about
patient privacy and algorithmic fairness. For example, previous work has shown
that patient self-reported race can be predicted from medical data that does
not explicitly contain racial information. However, the extent of data
identification is unknown, and we lack ways to develop models whose outcomes
are minimally affected by such information. Here we systematically investigated
the ability of time-series electronic health record data to predict patient
static information. We found that not only the raw time-series data, but also
learned representations from machine learning models, can be trained to predict
a variety of static information with area under the receiver operating
characteristic curve as high as 0.851 for biological sex, 0.869 for binarized
age and 0.810 for self-reported race. Such high predictive performance can be
extended to a wide range of comorbidity factors and exists even when the model
was trained for different tasks, using different cohorts, using different model
architectures and databases. Given the privacy and fairness concerns these
findings pose, we develop a variational autoencoder-based approach that learns
a structured latent space to disentangle patient-sensitive attributes from
time-series data. Our work thoroughly investigates the ability of machine
learning models to encode patient static information from time-series
electronic health records and introduces a general approach to protect
patient-sensitive attribute information for downstream tasks.
